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Assessing applicants for faculty positions in universities involves many issues. Each issue may involve a judgment based on uncertain or imprecise data. The uncertainty in data may exist in the interpretation made by the evaluator. This issue might lead to improper decision making. Modeling such a system using fuzzy logic will provide a more efficient model for handling imprecision. This article presents a fuzzy system for modeling the assessment of applicants for employment at academic universities. This system will utilize a multi-stage fuzzy model for measuring and evaluating the applicants. Utilizing fuzzy logic for applicants' evaluation will help administrators in choosing the best candidates for faculty positions. The fuzzy system was developed using jFuzzyLogic Java library. The reliability of the proposed system was proved by evaluating real-world case studies to prove its effectiveness to mimic human judgment. Moreover, the developed system has been evaluated by comparing it with a traditional mathematical method to prove the credibility and fairness of the proposed fuzzy system.

This article focuses on the problem of resilient H∞ filtering for Takagi-Sugeno fuzzy-model-based nonlinear networked systems with multisensors. A weighted fusion approach is adopted before information from multisensors is transmitted over the network. A novel event-triggered mechanism is proposed, which allows us not only to reduce the data-releasing rate but also to prevent abnormal data being potentially transmitted over the network due to sensor measurement or other practical factors. The problem of denial-of-service (DoS) attacks, which often occurs in a communication network, is also considered, where the DoS attack model is based on an assumption that the periodic attack includes active periods and sleeping periods. By employing the idea of the switching model for filtering error systems to deal with DoS attacks, sufficient conditions are derived to guarantee that the filtering error system is exponentially stable. Simulation results are given to demonstrate the effectiveness of the theoretical analysis and design method.

In this article, based on polynomial differential inclusions, we propose a heuristic iterative approach for estimating the domains of attraction for nonpolynomial systems. First, we use the fuzzy model to construct a polynomial differential inclusion for the nonpolynomial system, which can be equivalently written as a time-invariant uncertain polynomial system. Then, beginning with an initial inner estimation, we present an iterative approach to enlarge this initial inner estimation by calculating common Lyapunov-like functions. Furthermore, the domains of attraction are estimated by combining this iterative approach with heuristic construction of differential inclusions. In the end, our heuristic iterative approach is implemented with linear semidefinite programming and then tested on some nonpolynomial examples with comparisons to the existing methods in the literature.

In this brief, the resilient ${H_{\infty}}$ filter design problem for continuous-time nonlinear systems is addressed. A Takagi-Sugeno (T-S) fuzzy model with norm-bounded uncertainties is used to represent the nonlinear plant. Meanwhile, the fuzzy filter to be designed is assumed to have gain variations. A useful matrix inequality decoupling approach is proposed to separate the product terms with different types of uncertainties. Then, the resulting design condition of the resilient ${H_{\infty}}$ filter is described by strict linear matrix inequalities (LMIs). Compared with the existing fuzzy resilient filtering results, the proposed design method shows a strong advantage of reducing design conservatism. Finally, a simulation example is provided to demonstrate the feasibility and the advantage of the proposed design method.

It is prominent important for managers to assess the personal risk of mental patients. The evaluation process refers to numerous indexes, and the evaluation values are general portrayed by uncertainty information. In order to conveniently model the complicated uncertainty information in realistic decision making, interval-valued complex Pythagorean fuzzy set is proposed. Firstly, with the aid of Einstein t-norm and t-conorm, four fundamental operations for interval-valued complex Pythagorean fuzzy number (IVCPFN) are constructed along with some operational properties. Subsequently, according to these proposed operations, the weighted average and weighted geometric forms of aggregation operators are initiated for fusing IVCPFNs, and their anticipated properties are also examined. In addition, a multiple attribute decision making issue is examined under the framework of IVCPFNs when employing the novel suggested operators. Ultimately, an example regarding the assessment on personal risk of mental patients is provided to reveal the practicability of the designed approach, and the attractiveness of our results is further found through comparing with other extant approaches.The main novelty of the coined approach is that it not only can preserve the original assessment information adequately by utilizing the IVCPFNs, but also can aggregate IVCPFNs effectively.

PurposeThis paper addresses special reliability-centered maintenance (RCM) strategies for one-shot devices by providing fuzzy inferences system with the assumption that, to data, there is no data available on their maintenance. As far as one-shot devices are concerned, the relevant data is inadequate.Design/methodology/approachIn this paper, a fuzzy expert system is proposed to effectively select RCM strategies for one-shot devices. In this research: (1) a human expert team is provided, (2) spatial RCM strategies for one-shot devices and parameters bearing upon those strategies are determined, (3) the verbal variables of the expert team are transformed into fuzzy sets, (4) the relationship between parameters and strategies are designed whereupon a model is developed by MATLAB software, (5) Finally, the model is applied to a real-life one-shot system.FindingsThe finding of this study indicates that the proposed fuzzy expert system can determine the parameters affecting the choice of the appropriate one-shot RCM strategies, and a fuzzy inference system can help for effective decision making.Originality/valueThe developed model can be used as a fast and reliable method for determining an appropriate one-shot RCM strategy, whose results can be relied upon with a suitable approximation in respect of the behavior test. To the best authors’ knowledge, this problem is not addressed yet.

Activity recognition and classification are emerging fields of research that enable many human-centric applications in the sports domain. One of the most critical and challenged aspects of coaching is improving the performance of athletes. Hence, in this paper, the Adaptive Evolutionary Neuro-Fuzzy Inference System (AENFIS) has been proposed for sports person activity classification based on the biomedical signal, trial accelerator data and video surveillance. This paper obtains movement data and heart rate from the developed sensor module. This small sensor is patched onto the user’s chest to get physiological information. Based on the time and frequency domain features, this paper defines the fuzzy sets and assess the natural grouping of data via expectation-maximization of the probabilities. Sensor data feature selection and classification algorithms are applied, and a majority voting is utilized to choose the most representative features. The experimental results show that the proposed AENFIS model enhances accuracy ratio of 98.9%, prediction ratio of 98.5%, the precision ratio of 95.4, recall ratio of 96.7%, the performance ratio of 97.8%, an efficiency ratio of 98.1% and reduces the error rate of 10.2%, execution time 8.9% compared to other existing models.

Sport training is a sporting performance preparation phase that consists of four parts: Training in conditioning, technical Training, Training in attitude, Training in psychology. The challenging factor of the sports training session is the realization of approximate and uncertainty. The valued idea involves the definition of fuzzy sets and rules and membership functions to overcome the challenges. A fuzzy logical explanation enables successful ambiguous situations, which are complex, continuous, and more practical, closer to the real world and human thought. The concept, which is highly valued, includes defining fluids and rules and membership functions to test training exercises for strength. This paper proposed a Fuzzy, assisted artificial intelligence monitoring framework (FAAIMF) that evaluates all athletes’ behaviors in an outdoor training setting using wearable inertial sensors. Data obtained from sensor fitted machines, feedback, and proper implementation requirements are considered in the design. The Random Forest Classifier uses a Discreet Transform Wavelet (DWT) to effectively and accurately effectively and accurately id. Second, the relative orientation of the wearable inertial sensors on a shield and thigh of a material from which the knee angle of flexion-extension is determined. The proposed method in various non-constrained settings for the exact classification of sports activities and accurate movement techniques assessment.

This paper investigates the aperiodic sampled-data control for a chaotic system. Firstly, Takagi–Sugeno (T-S) fuzzy models for the chaotic systems are established. The lower and upper bounds of the sampling period are taken into consideration. Then, the criteria for mean square exponential stability analysis and aperiodic sampled-data controller synthesis are provided by means of linear matrix inequalities. And the real sampling patterns can be fully captured by constructing suitable Lyapunov functions. Finally, an illustrative example shows that the proposed method is effective to guarantee that the system’s states are stable with aperiodic sampled data.

In the process of people’s development, many resources in natural resources provide convenience and help for people’s survival and development, and forest is an important part of ecological balanced development. In the process of economic development, forest resources can also bring people a lot of economic benefits. People develop regional forestry economy by planting trees with economic value. Asset assessment of forests can better promote the development of forestry economy and help people better use and manage resources. During the process of forestry economic development, the technology of asset evaluation established on the basis of BP neural network enriches the theoretical content of forestry economic development to a certain extent and provides a more solid theoretical basis for development. At the same time, the qualitative and quantitative methods are combined to ensure that the data are more accurate and the key influencing factors can be better analyzed. Based on the analysis of the relevant research results, this paper takes some trees of the same growth year as the object of study. Using BP neural network to analyze the factors that will change the overall value of forest resources, a special BP neural network model belongs to forestry and economic development is established.

In this article, a Takagi–Sugeno fuzzy model is applied to deal with the problem of observer-based control design for nonlinear time-delayed systems with fractional-order [Formula: see text]. By applying the Lyapunov–Krasovskii method, a fuzzy observer–based controller is established to stabilize the time-delayed fractional-order Takagi–Sugeno fuzzy model. Also, the problem of disturbance rejection for the addressed systems is studied via the state-feedback method in the form of a parallel distributed compensation approach. Furthermore, sufficient conditions for the existence of state-feedback gains and observer gains are achieved in the terms of linear matrix inequalities. Finally, two numerical examples are simulated for the validation of the presented methods.

An q-rung orthopair fuzzy set is a generalized structure that covers the modern extensions of fuzzy set, including intuitionistic fuzzy set and Pythagorean fuzzy set, with an adjustable parameter q that makes it flexible and adaptable to describe the inexact information in decision making. The condition of q-rung orthopair fuzzy set, i.e., sum of qth power of membership degree and nonmembership degree is bounded by one, makes it highly competent and adequate to get over the limitations of existing models. The basic purpose of this study is to establish some aggregation operators under the q-rung orthopair fuzzy environment with Einstein norm operations. Motivated by innovative features of Einstein operators and dominant behavior of q-rung orthopair fuzzy set, some new aggregation operators, namely, q-rung orthopair fuzzy Einstein weighted averaging, q-rung orthopair fuzzy Einstein ordered weighted averaging, generalized q-rung orthopair fuzzy Einstein weighted averaging and generalized q-rung orthopair fuzzy Einstein ordered weighted averaging operators are defined. Furthermore, some properties related to proposed operators are presented. Moreover, multi-attribute decision making problems related to career selection, agriculture land selection and residential place selection are presented under these operators to show the capability and proficiency of this new idea. The comparison analysis with existing theories shows the superiorities of proposed model.

This paper investigates the switching control issue for uncertain vehicle suspension systems, meanwhile, actuator failure is taken into account. Takagi-Sugeno (T-S) fuzzy model is utilized to describe the considered uncertain suspension systems. Consider that most existing analysis results related to suspension systems are independent of membership functions, which may cause conservatism, thus, a novel membership functions-dependent Lyapunov function is employed in the stability analysis. In the proposed analysis technique, both of the information of membership functions and their derivatives are applied to reduce the conservativeness. A switching control strategy is proposed to assure that suspension constraint requirements are satisfied by using the information of time-derivative of membership. Lastly, a simulation example is shown to identify the availability of the approach put forward in this paper.

In this study, AA5083 plates were friction stir processed to improve the hardness by refining the grains and dispersing the β phase in the matrix. Tool rotation speed, tool traverse speed, and tool shoulder diameter were varied as per the face-centered central composite design and FSP trials were performed. The wear resistance of the friction stir processed specimens was estimated by conducting a wear test using a pin-on-disc tribometer. It was observed that the wear resistance was higher than the base specimens and TRS, TTS and SD had a significant effect on the wear resistance. An artificial neural network model and a fuzzy logic model were developed to predict the wear resistance of the friction stir processed specimens. As the Sugeno Fuzzy model had greater prediction efficiency than the ANN model, it was used to study the effect of FSP process parameters on the wear resistance of the specimens. The results indicate that a TRS of 1000 rpm, TTS of 30 mm/min, and SD of 18 mm maximized the wear resistance of the processed plate to 8820 N-m/kg.

This paper investigates the issues of extended dissipativity performance and stabilization for T–S fuzzy model (TSFM) based wind power generation systems (WPGSs). Firstly, the stochastic coupled leakage time-varying delays (CLTVDs) and randomly occurring uncertainty parameters (ROUPs) are firstly introduced for constructing more general TSFM. Second, on basis of the time-delay-product function (TDPF) and looped function strategy, a relaxed Lyapunov–Krasovskii functional (LKF) with the negative definite term and the time-varying matrix is developed, which can get the utmost out of the information of various communication delays. Third, by utilizing the tighter integral inequalities and reciprocally convex combination technique (RCCT), new stabilization criteria are established in terms of the linear matrix inequalities (LMIs). Simultaneously, the desired fuzzy sampled-data control (FSDC) is designed under the state quantization mechanism. Finally, a simulation example is presented to validate the efficiency of the proposed result.

Social Networking Sites (SNS) have entailed a revolution for society. They have given a say to everyone regardless of their status and this has been translated into loads of data. The task of profiling users constitutes a way to learn from this data in order to show users only the content that is relevant to them. Several recommendation system techniques have been used to address this problem, being mainly based on what the user explicitly says about themselves, on what the user publishes in the SNS and on the similarities between users. However, in social media context, it is also possible to use relations between users. Considering basic relations like follower or followee to extract information from them may result in noise, since they do not imply that users share interest or even ideas. In this work, we present a fuzzy framework to enrich user profiles with complex properties in order to have an even better representation of them. We use basic relations defined by SNSs to complete the information available in user profiles with topics of interest and ideas towards them and to define deep relations that will enable new ways of analysis. We use these deep relations to create clusters of similar users that, ultimately, will allow the expansion of properties from known users to the rest of the cluster. We tested our proposal with a dataset of Tweets in Spanish related to a political event. Our experiments prove the potential that this approach has for a lot of applications in microblogging context.

The subcontractor selection decision is inherently a multicriterion problem. It is a decision of strategic importance for companies. The nature of this decision is usually complex and unstructured. Management science techniques might be helpful tools for solving these kinds of decision-making problems. In this research, the fuzzy logic method and the analytic hierarchy process were applied for the selection of suitable subcontractors in an apparel supply chain. In general, many factors, such as quality level, price offer, and delivery delay, were considered to determine the most suitable and reliable subcontractors that fit the company's strategy. This survey is carried out using the database of an apparel company manufacturing denim products.

The construction of the evaluation index system of employee satisfaction level is the basis for an enterprise to measure the level of employee satisfaction. In this paper, the author analyzes the employee incentive in supply chain network based on asymmetric information game analysis and fuzzy model. Through the correction of network weight and threshold value, the error function decreases along the gradient direction. The construction and training of the network can be realized by MATLAB. The newff function in the software is used to construct the network, and the train function is used to train. This evaluation index system is a multi-level target evaluation system based on asymmetric information game, and its basic principle is to simplify complex problems. Long term effort is bigger when the salesperson’s risk averse parameter is more than a certain value or risk averse parameter is less but discount factor is more than a threshold; short term effort is bigger when risk averse parameter and discount factor is both less. For both information scenarios, the compensation contracts are designed and comparison analyses are conducted.

Floods are a climatic phenomena that affect different regions worldwide and that produces both human and material losses; for example in 2017, six of the worst floods were the cause of 3.273 deaths worldwide. In Colombia, the strong winter wave presented between 2010 and 2011, caused 1,374 deaths and 1,016 missing persons. The main river in Colombia is the Magdalena, which provides great benefits to the country but is also susceptible to flooding. This article presents a proposal to optimize a fuzzy system to prevent flooding in homes adjacent to areas of risk to the Magdalena River. The method used is based on evolutionary algorithms to perform a global search, including a gradient-based algorithm to improve the solution obtained. The best result achieved was the Mean Square Error (MSE) of 7, 83E - 05. As a conclusion, it is needed to employ optimization methods for the adjustment of parameters of the fuzzy system when considering that the sets and the rules are systematically obtained.

Fuzzy systems are widely used for solving complex and non-linear problems that cannot be addressed using precise mathematical models. Their performance, however, is critically affected by how they are constructed as well as their fuzzy rule base. Inspired by neural networks that apply a multi-layer structure to improve their performance, we propose a multi-layer fuzzy model with modified fuzzy rules to improve the approximation ability of fuzzy systems without losing efficiency. In practical applications, the fuzzy rule base extracted from numerical data is often incomplete, which makes a fuzzy system less robust. To address this problem, a non-linear function is used as the consequent of each fuzzy rule based on fuzzy-rule clustering to enhance the approximation ability of the fuzzy rule base. In addition, exact matching of fuzzy rules is employed based on the fuzzy rule’s antecedent for prediction. By doing so, only one rule will be triggered in each layer, which is very efficient. Experimental results from two simulated functions and three practical applications confirm that our proposed multi-layer fuzzy model can outperform other well-established fuzzy models in terms of accuracy and robustness without sacrificing efficiency.

The effect of eutrophication is characterized by dense algal and plant growth due to the enrichment of nutrients for photosynthesis. As a result, it often plays an important role to the formation of plants that float in the surface of a water body. When nutrients are increasing in aquatic ecosystems, the photosynthetic plants grow rapidly. As a result, the algae limit the amount of dissolved oxygen required for respiration by other species in the water. Multi-criteria analysis has helped us towards the understanding and estimation of all physical, chemical and biological functions. In this paper, the examined water body, as a rich and variable system, is an ideal case for our study. Our purpose is to investigate some of the factors responsible for eutrophication (water temperature, nitrates, total phosphorus, Secchi depth, chlorophyll-a) using fuzzy logic. In this method, there are infinite numbers of fuzzy implications which can be used, since the proposition can take any value in the close interval [0,1]; hence, the investigation of the most appropriate implication is required. In this paper, we propose a method of evaluating fuzzy implications constructing triangular fuzzy numbers for all of the studied factors coming from statistical data. The deviation of the true value is the key for the selection of the most appropriate fuzzy implication describing the functions and the mechanisms in this ecosystem.

The studying motivation of this paper is that there exist many modeling issues of non-uniformly sampling nonlinear systems in industrial systems. Based on multi-model modeling principle, the corresponding model of non-uniformly sampling nonlinear systems is described by the nonlinear weighted combination of some linear models at local working points. Fuzzy modeling based on multimodel scheme is a common method to describe the dynamic process of non-linear systems. In this paper, the fuzzy modeling method of non-uniformly sampling nonlinear systems is studied. The premise structure of the fuzzy model is confirmed by GK fuzzy clustering, and the conclusion parameters of the fuzzy model are estimated by the recursive least squared algorithm. The convergence perfromance of the proposed identification algorithm is given by using lemmas and martingale theorem. Finally, the simulation example is given to demonstrate the effectiveness of the proposed method.

Entrepreneurs’ need to manufacture quality products at a reasonable cost with the goal of enhancing their competitive edge in the market has led to an increase in the development and use of prevent...

In this paper, an event-triggered disturbance attenuation and rejection scheme is proposed for nonlinear networked systems with limited bandwidth and multiple disturbances based on the interval type-2 Takagi-Sugeno fuzzy model. A fuzzy disturbance observer is constructed to estimate the disturbance generated by the exogenous system, and then such kind of disturbance can be rejected. Sufficient conditions are presented such that a fuzzy H∞ controller can be designed to guarantee the stability of the resulting closed-loop system with a prescribed performance index, regardless of disturbances. An event-triggered control scheme is designed to reduce the frequency of the controller updating and thus save the network resources. A numerical example is given to demonstrate the validity of the new design methods presented.

An experimental study combined with a mathematical modeling of vacuum drying of cocoa beans was carried out to enhance the drying kinetics without compromising the quality of dried products. Experiments were conducted to compare the effects of variation of temperature and pressure on the drying process. The temperature was varied between 40 and 60 °C, while the pressure between 735 and 245 mmHg. The experimental study investigated and analyzed the effects of various process parameters on drying kinetics and quality (in terms of color and pH value). Results demonstrated that the cocoa beans dried at 50 °C temperature and 735 mmHg pressure had the highest drying kinetics, lowest acidity, and best color among all the temperature and pressure values. Compared to convective drying conducted at 60 °C temperature and atmospheric pressure (760 mmHg), vacuum oven drying consistently showed better drying performance with a higher quality of dried products. Modeling for vacuum drying kinetics of cocoa beans was performed with semi-theoretical drying kinetic and fuzzy models to verify the experimental results. The graphical representations of the results demonstrated a good fit between the fuzzy model and the experimental data compared with semi-theoretical drying kinetic models. Finally, a suitable model was proposed for optimum drying conditions compared with the fuzzy and drying kinetic models that could play an important role in designing future advanced dryers.

This paper deals with the problem of designing a two-dimensional (2D) modified repetitive-control system based on a Takagi–Sugeno (T-S) fuzzy model to achieve high tracking performance for a nonlin...

Most green supply chain models focus on manufacturing and remanufacturing, but do not consider the refurbishing activities. Refurbished products are those that are slightly damaged or have some tec...

The approach has been developed to determining the numerical value of a failure probability and to forecasting the resource of an instrument transformer cell at the time of observation. Underlying a given approach is the control over the main parameters that affect the technical condition (TC) of an instrument transformer cell in the distributing device of high voltage (DDHV). To determine a TC of the devices, a mathematical method of fuzzy modeling has been applied, which makes it possible to integrate the diagnostic parameters that are different in their nature. Building a fuzzy model involved the experience of experts in the relevant industry.The relevance of the development of a given approach is predetermined by the functional importance of a current transformer. Its performance affects the accuracy of triggering the relay protection devices, as well as the accounting of electrical energy. Precise accounting of electric energy implies minimizing its losses and shows the path to energy savings. A special feature of this approach is that it takes into consideration the influence of TC of each piece of cell equipment on the probability of its failure in general. To account for the factors of random disturbances, an expert fuzzy model is refined by the probabilistic-statistical method.An example of the DDHV instrument transformer cell in an electric-energy system has been used to substantiate the advantage of a given approach over the existing methods to control the technical condition of electrical equipment. The error in predicting the cell resource based on one parameter (thermal imaging examination) was Δf(D-02)=1–QD-02=0.364, or 36.4 %. When applying an expert-statistical model for determining the probability of a cell failure, the error was Δf(D-02)=1–Qapost=0.034, or 3.4 %. The application of a given approach has produced a more reliable estimate of the probability of cell failure.Implementing the developed approach in the field of electrical equipment diagnosing could improve the reliability level of forecasting results. The constructed model could be applied in the automated systems that diagnose "on-line" the DDHV electric devices, Разработан подход определения численного значения вероятности отказа и прогнозирование ресурса ячейки измерительного трансформатора на момент наблюдения. Данный подход основан на контроле основных параметров, которые влияют на техническое состояние (ТС) аппаратов ячейки измерительного трансформатора распределительного устройства высокого напряжения (РУВН). Для определения (ТС) аппаратов применен математический метод нечеткого моделирования, который позволяет проинтегрировать разные по своей природе диагностические параметры. В построении нечеткой модели используется опыт экспертов соответствующей отрасли.Актуальность разработки данного подхода обусловлена функциональной важностью трансформатора тока. От его роботы зависит точность срабатывания устройств релейной защиты, а также учет электрической энергии. Точный учет электрической энергии, это минимизация ее потерь и путь к энергосбережению. Особенностью данного подхода есть то, что в нем учитывается влияние ТС каждой единицы оборудования ячейки, на вероятность ее отказа в целом. Для учета факторов случайных возмущений, экспертная нечеткая модель уточняется вероятностно-статистическим методом.На примере ячейки измерительного трансформатора РУВН электроэнергетической системы, обосновано преимущество данного подхода над существующими методами контроля технического состояния электрооборудования. Погрешность прогноза ресурса ячейки по одному параметру (тепловизионное обследование) составила Δf(Р-02)=1–QР-02=0,364 или 36,4 %. При применении экспертно-статистической модели в определении вероятности отказа ячейки погрешность составила Δf(Р-02)=1– Qapost=0,034 или 3,4 %. Применение данного подхода показало более достоверную оценку вероятности отказа ячейки.Внедрение разработанного подхода в отрасли диагностики состояния электрооборудования, может повысить уровень достоверности результатов прогнозирования. Разработанная модель может быть применена в автоматизированных системах «on-line» диагностирования электрических аппаратов РУВН, Розроблений підхід визначення чисельного значення ймовірності відмови та прогнозування ресурсу комірки вимірювального трансформатора на момент спостереження. Даний підхід заснований на контролі основних параметрів, які впливають на технічний стан (ТС) апаратів комірки вимірювального трансформатора розподільчого пристрою високої напруги (РПВН). Для визначення (ТС) апаратів застосований математичний метод нечіткого моделювання, який дозволяє проинтегрувати різні за своєю природою діагностичні параметри. В побудові нечіткої моделі використовується досвід експертів відповідної галузі.Актуальність розроблення даного підходу обумовлена функціональною важливістю трансформатор струму. Від його роботи залежить точність спрацювання пристроїв релейного захисту, а також облік електричної енергії. Точний облік електричної енергії – це мінімізація її втрат та шлях до енергозбереження. Особливістю даного підходу є те, що в ньому враховується вплив ТС кожної одиниці обладнання комірки, на ймовірність її відмови в цілому. Для врахування факторів випадкових збурень, експертна нечітка модель уточнюється ймовірнісно-статистичним методом. На прикладі комірки вимірювального трансформатора РПВН електроенергетичної системи, обґрунтовано перевагу даного підходу над існуючими методами контролю технічного стану електрообладнання. Похибка прогнозу ресурсу комірки за одним параметром (тепловізійне обстеження) склала Δf(Р-02)=1–QР-02=0,364 або 36,4 %. При застосуванні експертно-статистичної моделі в визначенні ймовірності відмови комірки похибка склала Δf(Р-02)=1–Qapost=0,034 або 3,4 %. Застосування даного підходу показало більш достовірну оцінку ймовірності відмови комірки.Впровадження розробленого підходу в галузі діагностиці стану електрообладнання, може підвищити рівень достовірності результатів прогнозування. Розроблена модель може бути застосована в автоматизованих системах «on-line» діагностування електричних апаратів РПВН

Based on the multi-model principle, the fuzzy identification for nonlinear systems with multirate sampled data is studied. Firstly, the nonlinear system with multirate sampled data can be shown as the nonlinear weighted combination of some linear models at multiple local working points. On this basis, the fuzzy model of the multirate sampled nonlinear system is built. The premise structure of the fuzzy model is confirmed by using fuzzy competitive learning, and the conclusion parameters of the fuzzy model are estimated by the random gradient descent algorithm. The convergence of the proposed identification algorithm is given by using the martingale theorem and lemmas. The fuzzy model of the PH neutralization process of acid-base titration for hair quality detection is constructed to demonstrate the effectiveness of the proposed method.

Control of slab camber is an important topic in hot strip rough rolling process. In this study, a slab camber control system, composed by a camber prediction model and a gap levelling value calcula...

This study proposes a method of designing quadratic optimal fuzzy parallel-distributed-compensation controllers for a class of time-varying Takagi–Sugeno fuzzy model–based time-delay control systems used to solve the finite-horizon optimal control problem. The proposed method fuses the orthogonal function approach and the improved hybrid Taguchi-genetic algorithm. The Taguchi-genetic algorithm only requires algebraic computation to perform the algorithm used to solve time-varying Takagi–Sugeno fuzzy model–based time-delay feedback dynamic equations. The fuzzy parallel-distributed-compensation controller design problem is simplified by using the Taguchi-genetic algorithm to transform the static parameter optimization problem into an algebraic equation. The static optimization problem can then be solved easily by using the improved hybrid Taguchi-genetic algorithm to find the quadratic optimal parallel-distributed-compensation controllers of the time-varying Takagi–Sugeno fuzzy model–based time-delay control systems. The applicability of the proposed integrative method is demonstrated in a real-world design problem.

This paper considers the yaw-moment control issue for uncertain electric vehicle systems with sensor failures and actuator saturation. By employing the interval type-2 (IT2) fuzzy approach, the uncertain electric vehicle systems are constructed as an IT2 fuzzy model. In this model, the parameter uncertainties are described through the membership functions (MFs) with lower and upper bounds. Then, an IT2 fuzzy state-feedback controller, which can share different MFs with the IT2 fuzzy model, is designed. The sensor failures can be quantified by a variable varying in a given interval. Meanwhile, the saturation nonlinearities can be handled by employing a norm-bounded strategy. By means of the Lyapunov stability approach, sufficient conditions of the controller design are derived to achieve the desired performance. Finally, simulation results based on the electric vehicle systems are presented to demonstrate the effectiveness of the proposed control scheme.

When a wheeled mobile robot is travelling at high longitudinal velocities and turning, it may sideslip. Except for a few researchers, this issue has not received much attention. However, there is a high possibility that sideslip may cause danger, so the authors think this problem needs to be solved urgently. The mechanism of preventing sideslip is simple, which is to reduce the longitudinal velocity of the robot when turning. But, how to slow down is a question worth studying. To this end, they have proposed two schemes, but these two schemes have poor real-time performance and poor robustness to positioning errors, respectively. In order to solve the above problems, considering that the real-time and robustness of fuzzy control are very good, they propose a controller by combining robust control and model predictive control in this Letter. The fuzzy control is used to adjust the longitudinal velocity according to the state of the robot, and the model predictive control is used to achieve path tracking. According to the simulation results, the proposed fuzzy model predictive controller does have small errors, small sideslip, good real-time performance, and good robustness to positioning errors.

This paper concerns robust stabilization of nonlinear fractional order interconnected systems. Based on uncertain fractional order Takagi–Sugeno fuzzy model and the fractional order extension of lyapunov direct method, a parallel distributed compensate controller is designed to asymptotically stabilize the fractional order interconnected systems. Then, a sufficient condition is given in the format of linear matrix inequalities. Simulation example is given to validate the effectiveness of the approach.

PurposeConstruction delays are a common problem in the Egyptian construction industry. Frequent delays in a project can lead to disputes and litigation. This paper aims to study and analyze the causes of construction delays.Design/methodology/approachThis research identifies delay causes in residential projects in Egypt and analyzes these causes with the relative importance index method. The feedback of construction experts was obtained through interviews. Subsequently, a questionnaire survey was prepared. Fifty-six different delay causes were identified and categorized into eight categories based on a literature review and interviews with 36 experts from the construction industry who represent owners, consultants and contractors.FindingsAccording to the highest values of the relative importance index, the top ten delay causes of residential projects in Egypt are determined. Statistical analysis is carried out using the analysis of variance method to test delay causes obtained from the survey, which revealed a good correlation between residential projects parties with respect to most of the delay causes. A fuzzy logic model using MATLAB is developed for determining the probability of delay of residential projects, and the model was tested in a real case study and was found to be satisfactory.Originality/valueDelay causes in residential projects in Egypt were identified, and these causes were analyzed using the relative importance index method and fuzzy logic.

Rockburst is the sudden ejection of highly stressed brittle rocks. This can impose major risks on underground projects. Despite all the studies conducted on this phenomenon since the 1900s, it is s...

This research focuses on the H∞ stability analysis and fuzzy dynamic output feedback control for a class of interval type-2 T-S fuzzy systems. Interval type-2 T-S fuzzy model is employed to approximate the uncertain system with multiple time-varying delays and external disturbance. Interval type-2 T-S fuzzy dynamic output feedback controller is designed by employing the interval type-2 T-S fuzzy model. Some stability criterions in terms of linear matrix inequalities (LMIs) are presented by introducing the fuzzy Lyapunov function. The closed-loop system is described as a descriptor system by employing the descriptor redundancy approach. Some less conservative stability criterions un-including the product terms between system gains and controller gains are presented and the fuzzy-basis-dependent matrix with system gains transformation is introduced. The stability criterions are extended to a finite set of LMIs constraints. Finally, simulation examples and comparison figures are presented to show the effectiveness of the proposed approach.

A T-S fuzzy model with two rules is established to exactly describe the nonlinear uncertain heave dynamics of underwater vehicles with bounded heave speed. A single linear-matrix-inequality-based (LMI-based) state feedback controller is then synthesized to guarantee the global stability of the depth control system. Simulation results verify the effectiveness of the proposed approach in comparison with linear-quadratic regulator (LQR) method. Nonlinear disturbance observer is appended to the system when the underwater vehicles are affected by the gravity-buoyancy imbalance. The two-stage control method is effective to stabilize an uncertain system with both parameter uncertainties and external disturbances.

This paper addresses security control for a class of Takagi-Sugeno (T-S) fuzzy systems with multi-sensor saturations and distributed event-triggered mechanism (DETM). As sensors can be located in different places, we will introduce a DETM to reduce the transmission rate of the network. The output measurements of each sensor are released into the network or not is dependent on the corresponding event-triggered condition. A new T-S fuzzy model is constructed, which characterizes the influences of multi-sensor saturations and cyber-attacks. Sufficient conditions are derived for ensuring the asymptotical stability of the augmented closed-loop system. The explicit expressions of the controller gains are obtained. Finally, the feasibility of the designed algorithm is shown by a numerical example.

Cancer disease is assumed as a gathering of diseases which is initiated because of uncontrolled cell growth. An early analysis of Lung Nodules (LN) can possibly enhance the prognosis and in future can save numerous lives every year. In the proposed research work, the LN detection from the ELCAP lung image database is analyzed by image segmentation and classification techniques. Initially, the exact portion of the lung image is achieved and then it is subjected to pre-processing where the image contrast level is enhanced by the imadjust function of MATLAB. Next to that, the potential nodules are segmented by the Fuzzy C-Means (FCM) and then some features are extracted for effective classification. Based on the selected or extracted feature sets, the images are classified as two types (nodule detected and normal lung) by the proposed classifier i.e. Artificial Neural Network (ANN) with weight optimization. The performances of the proposed algorithm and classifier are tested on the chosen datasets in terms of sensitivity, specificity and accuracy. The results demonstrate that ANN with Oppositional based Ant Lion Optimization (OALO) algorithm achieves high accuracy and less execution time compared to existing algorithms.

In this paper, we consider various approaches to the problem of filtering in nonlinear dynamic systems and their algorithms. The Strong Tracking Unscented Kalman Filter, based on the combination of Unscented Kalman Filter and Strong Tracking Kalman Filter, provides stability to the uncertainty of the process model directly using a suboptimal scaling factor (SSF). The softening coefficient is part of the SSF and it improves the smoothness of the system state assessment. The coefficient is determined empirically and is included in the entire filtering process, which leads to a loss of accuracy in the time segments in which the process model is defined. The paper explores the use of Takagi --- Sugeno fuzzy model (T-S model) to adjust in real time the softening coefficient when the object's dynamics changes. As a result of a comparative analysis of the accuracy of the studied filters for the nonlinear model, it was found that the new filter using a fuzzy logical adaptive system possesses good smoothness of assessment and the greatest accuracy

In this paper, we have carried out a new method for the anti-synchronization of a fractional-order hyperchaotic Duffing system using the nonlinear predictive control. At first, in this method master and slave systems are modeled using Takagi–Sugeno fuzzy model, Then, nonlinear predictive controller based on proposed model, is applied to the system to achieve the aim of the anti-synchronization. In this controller, the feedback coefficients of the error are set using the teaching learning based optimization, so that the cost function, which is including the error and control effort, becomes minimized. The advantage of the proposed method is the robustness of the controller against uncertainty and disturbance. Simulating results showed that, the proposed method has been able to anti-synchronize the master and slave fractional-order hyperchaotic Duffing systems with uncertainty and disturbance in highly accurately manner.

This paper deals with the fuzzy control design for nonlinear impulsive switched systems modeled by a novel nonlinear Takagi-Sugeno (T-S) fuzzy structure. To model, this structure only uses some of the nonlinearities as premise variables. So, the derived model has fewer rules compared with the traditional T-S fuzzy models that utilize standard local sector nonlinearity; and accordingly, the number of stability/stabilization conditions is sharply reduced. In our structure, considering only a part of nonlinearities as premise variables causes that some of the local models in the consequent part of the fuzzy rules be nonlinear but with less complexity than the original nonlinear system. As a result, the feasibility of our model’s stability criteria is more than those that one can directly establish for the original nonlinear system. Besides, unlike the existing methods in the literature of impulsive switched systems that aim to permanently reduce the value of Lyapunov function candidates, this paper takes into account this process until trajectories reach a sufficient small region containing the origin. Then, within this region, the size of Lyapunov functions is just controlled at impulse instants. This strategy is useful when there are non-vanishing impulses and/or uncertainties, and it is more relaxed when the goal is ultimate bound stability. Finally, to achieve the stabilizing control signal that ensures practical control issues along with smallest ultimate bound and widest region of attraction, this paper also proposes an optimization problem with linear and bilinear constraints. The simulation results represent the performance of the proposed method.

This paper addresses the tracking accuracy and robustness enhancement problems of fuzzy model based predictive control (MPC) for a class of nonlinear systems subjecting to lumped disturbances composed of bounded unknown disturbances and a model-plant mismatch. Main features of the proposed method are: 1) A fuzzy disturbance observer and an auxiliary controller are jointly developed to meet a certain control objective that minimizes the peak bound of the errors caused by the lumped disturbances, which eventually leads to desired offset-free tracking performance. 2) A pre-computed robust positively invariant set whose central is the nominal state is derived with the premise of input-to-state stability. 3) Tightened constraints for the guarantee of recursive feasibility of MPC is computed off-line and the quasi-min-max fuzzy MPC is elaborately designed according to a piecewise Lyapunov function. Furthermore, characteristics of robustness enhancement and low on-line computational burden are obtained as compared with the existing offset-free MPCs, and further the impacts of estimation error arising from sampling time and admissible target set on the system performance are also discussed. Two simulation examples verify the effectiveness of the proposed approach ensuring the satisfaction of constraints.

This paper addresses the robust H∞ filter design problem for a class of uncertain fuzzy neutral stochastic system with time-delay through Takagi–Sugeno (T–S) fuzzy model. By constructing an augmented Lyapunov–Krasovskii functional, some novel delay-dependent stability criteria for uncertain fuzzy neutral stochastic system with time varying delay are obtained in terms of linear matrix inequalities. By using the integral inequality in the neutral stochastic setting combined with delay decomposition approach, the H∞ fuzzy filter is designed to guarantee the corresponding filtering error systems robustly asymptotically stable with a specified H∞ performance index. At last, two numerical examples are presented to show the less conservatism than the previous results.

In this paper, the problem of stabilization with optimal L1-gain for positive T-S fuzzy systems is investigated with the use of linear Lyapunov function. A T-S fuzzy model for positive nonlinear system is established to study the stabilization control for the positive system. Sufficient condition for stabilization is presented in term of linear programming. The static output-feedback fuzzy controller is constructed to guarantee that the closed-loop system is controlled positive, asymptotically stable and the L1-gains from the exogenous inputs to the regulated output is minimized, respectively. Moreover, the stabilization problem with optimal L∞-gain for positive T-S fuzzy systems is solved. Finally, three examples are presented to show the effectiveness of the theoretical results.

Purpose Nowadays, there are various internet of things (IoT) applications covering many aspects of daily life. Many people own numerous smart objects that use these IoT applications. The purpose of this study is determining suitable IoT applications for each user which is a relevant challenge because it is amulti-criteria decision-making. Design/methodology/approach To solve this challenge, the authors propose fuzzy analytical hierarchy process model. Based on the opinions of IoT experts, the model and the hierarchy were designed to assess and compare three crucial IoT criteria, namely, object, application and providers. Findings The results indicated that the application criterion is far more relevant for users other than the two criteria. The findings of this study offer insights into more effective decision-making for IoT application developers and providers. Originality/value This study contributes to the IoT through proposing a fuzzy model to classify IoT applications. The findings provide meaningful implications for IoT application providers.

This paper investigates the event-based control for networked T-S fuzzy cascade control systems with quantization and cyber attacks. In order to solve the problem of limited communication resources, an event-triggered scheme and a quantization mechanism are adopted, which can effectively reduce the burden of communication and save the network resources of the system. By considering the influence of cyber attacks, a newly quantized T-S fuzzy model for networked cascade control systems (NCCSs) under the event-triggered scheme is established. By using the Lyapunov stability theory, sufficient conditions guaranteeing the asymptotical stability of networked T-S fuzzy cascade control systems are obtained. In addition, the controller gains are derived by solving a set of linear matrix inequalities. Finally, a numerical example is presented to verify the validity of the proposed method.

The present paper solves the approximation problem of T–S fuzzy linear singular system for a class of nonlinear singular system with impulses. Consider a special nonlinear singular bio-economic system with impulses; the T–S fuzzy linear singular system of the nonlinear singular system has been calculated. The relationship between the impulse of the singular system and the singular induced bifurcation is proved for the first time. For this particular case, it is extended to more generally nonlinear singular system. For a class of nonlinear singular system that is bounded impulse-free item and separable impulse item with singularity-induced bifurcation, we proved that it can be approximated by T–S fuzzy singular system with arbitrary accuracy. Finally, a numerical simulation is carried out to show the consistency with theoretical analysis and illustrate the effectiveness of approximation.

The increased popularity of smart home technologies, lower prices of technical infrastructure and the growing demand for these technologies accelerated the development of smart systems. Automatic irrigation of the soil with the help of automatic systems will prevent sufficient irrigation of the soil in dry periods. This can cause the cultivated area and garden plants wither and eventually die. In this study, a smart plant irrigation system is developed, aiming to address the need for these systems in such cases. The system here is created using various sensors, and, through application of a fuzzy model, it can determine the amount of water required for a specific period. The fuzzy model determines the exact amount of water needed by the plant, hence prevents undesirable situations like excessive or insufficient watering of the plant. This enables planning for soil irrigation. The system proposed in this paper is a low-cost and easy-to-install one that can be applied in any agriculture.