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Introduction: Organizations, manufacturing and non-manufacturing companies, and development units are always associated with incidents and the need for proper risk assessment. One of these is the process of constructing subwayways that, given its nature, require special attention and the occurrence of accidents is always there. But as risk definition is clear, risk is always associated with uncertainty. Accordingly, it should be noted that the performance of risk assessment calculations should be consistent with the principles that could cover this uncertainty. One of the most prominent methods in this field is the use of fuzzy logic fuzzy calculations that can provide an appropriate response, if accompanied by expert opinions, to estimate the probable risk of accidents. Methods: In this paper, firstly, identification of the existing occupational hazards in the construction of subway projects was carried out. Then, prioritization and ranking of identified hazards were performed based on the degree of importance of the fuzzy hierarchy analysis method, and finally using the method of analysis and the fuzzy hierarchical analysis of ranking has been done on the safety risks of the subway construction. Results: The results show that the main risk factors of these operations are priorities, including depletion during drilling, the collision of machinery with manpower is associated with hazards associated with shotcrete and surface water and groundwater operations. Conclusion: In the present article, a new model was developed to calculate the probability of occurrence of the event, which so far has not been addressed in other studies. Finally, effective measures can be taken to reduce the risk of a project by eliminating the high risk factors.

This work aims to develop and test a computational mathematical model, based on the fuzzy set theory, to predict the rate of thermal comfort by means of the swine behavior in relation to their age and to the black globe humidity index for two nursery types. Nursery 1 had brick stalls with fully slatted metal flooring, and nursery 2 had wooden stalls with fully slatted plastic flooring. Nursery style 2 presented a higher frequency of behavior in the condition comfort than nursery style 1, respectively 39,32% and 38,16%. The thermal comfort values for the fuzzy system were 3,58% for the standard deviation and 72,86% for the coefficient of determination. The developed fuzzy model has proven adequate in predicting thermal comfort by means of the animal's behavior.

The article deals with the problem of analysing implicit factor influence. It later reveals a mathematical model for conducting a search for implicit factors within a system which is based on a fuzzy set theory and employs fuzzy binary relations and is put into practice as a combination of web-services posted on http://bi.usue.ru/. The model's capability to identify implicit factors by making use of expert judgements presented in natural language is lastly demonstrated.

Composite materials have the perk of combining a number of properties, which are not usually found together in a single material. The Hybrid Fiber composite solves the increasing need for Eco friendly materials coupled with high durability and strength. HFC has a wide range of applications in the field of aerospace, auto motives, civil constructions and electronics at a cost, which is lower than that of the traditional fibers like GFRP. Drilling is one of the primary operations adopted by industries for material working and component building. In this paper, the seminal process parameters - spindle speed, feed rate and the drill diameter are analysed and a Fuzzy model is burgeoned predicting the Thrust force and Torque for drilling Hybrid fiber Composite (HFC). The results indicate that the Fuzzy prediction model is effective and facilitates a better and easier artificial intelligence modelling.

This work is directed to forecast the number of foreign visitors come to tourist’s destinations at Badung regency, Province of Bali. Using visit historical data for period January 2000 to February 2015, Markov Transition Matrix and Fuzzy Triangular Number are applied to represent fuzzy logical relationship group and member function in fuzzy model, respectively. The results showed the in-sample forecasting accuracy for our fuzzy model as much as 2,48 percent. To validate the model, we found the average forecasting error rate for five consecutive months (March – July 2015) as much as 2,70 percent.

Intelligent control systems are actively developing and can significantly reduce financial costs and improve the environmental performance of ore-dressing processes. Usage of intelligent control systems for gravitational enrichment allows to minimize costs and negative influence of ore tails on the environment, exclude losses of concentrate and energy resources in the ore-dressing process. The paper discusses the problems and prospects for the application of intelligent control systems of gravity concentration equipment. A structure of the control system and an intelligent model for determining the frequency of pulsation jigging machine based on fuzzy logic are proposed. The model is based on data obtained from experts, it is researched. The obtained results allow to judge about the prospects for the implementation of intelligent systems in the management of ore enrichment.

This paper proposes a new switching adaptive fuzzy controller and applies it to vibration control of a vehicle seat suspension equipped with a semi-active magnetorheological (MR) damper. The proposed control system consists of three functioned filters: (1) Filter 1: a model of interval type 2 fuzzy to compensate disturbances; (2) Filter 2: a ‘switching term’ to evaluate the magnitude of disturbance; and (3) Filter 3: a group of adaptation laws to enhance the robustness of control input. These filters play a role of powerful shields to improve control performance and guarantee the stability of the applied system subjected to external disturbances. After embedding a PID (proportional-integral-derivative) model into Riccati-like equation, main control parameters are updated based on the adaptation laws. The proposed controller is then synthesized in two different cases: high disturbance and small disturbance. For the high disturbance, a special type of sliding surface function, which relates to an exponential function and its t-norm, is used to increase the energy of control system. For the small disturbance, the energy from the modified t-norm of the sliding surface is neglected to reduce the energy consumption with maintaining the desired performance. To demonstrate the effectiveness of the proposed controller, a vehicle seat suspension installed with controllable MR damper is adopted to reflect the robustness against external disturbances corresponding to road excitations. It is validated from computer simulation that the proposed controller can provide better vibration control performance than other existing robust controllers showing excellent control stability with well-reduced displacement and velocity at the position of the seat.

With the rapid expansion of chain network, enterprises meet the consumption demand scattered around in a large range. In this paper, SOM neural network algorithm is introduced for empirical test. Design includes the structure of the fuzzy neural network identification and parameter identification, structural identification include input space division and the number of fuzzy rules to determine. Through summarizing and analyzing the characteristics of chain retail enterprises, this paper proposes to build a hierarchical and differentiated incentive mechanism by cultivating retail culture. The result shows that the knowledge staff is been higher the education level, the work creativity is stronger, cooperates the demand to the team members. In the era of the knowledge economy, knowledge has replaced capital as the core source of the enterprise core competence. The performance evaluation of knowledge workers is complex and the performance of the general staff is often easier to get a more objective evaluation. In conclusion, performance characteristics of knowledge workers should include general knowledge staff quality, knowledge staff performance behavior and performance results three aspects of characteristics. [ABSTRACT FROM AUTHOR]

The accelerated development of urbanization in China started with economic globalization and industrialization, and also in the process of economic system transition. Generally speaking, urbanization is an important indicator of a region's economic development and social development. Urbanization equity is an important direction of sustainable economic and social development. From the economic dimension, urbanization can promote division of labor, specialization and accumulation of human capital through agglomeration effect. This paper analyzes the social equity and urbanization by using fuzzy logic and factor analysis model. Under the condition of market economy, migrant workers have no competitive advantage in the labor market because of their low educational level. At the same time, the treatment of migrant workers in social security, cultural education and economic welfare is lower than that of urban residents. Under the unbalanced economic development reality, the economic absorption effect leads to the migrant workers rushing to the developed big cities. The results shows that by taking the dimensions of social justice of the rural migrant worker as the independent variable, psychological urbanization for the dependent variable, the regression Equation of F value is 90.424, P = 0.000, less than 0.05 level of significance. Also, we make correlation analysis on all factors of social justice and urbanization. The experimental results show the effectiveness of proposed method. Green building is the development of sustainable development concept in architectural field. While the construction industry has brought great benefits to the development of national economy, its high investment, high pollution and inefficient development mode has also produced a huge energy load. Therefore, from the perspective of environmental and economic sustainability, the development of green buildings is particularly important. In this paper, the author makes economic benefit analysis of green building based on fuzzy logic and bilateral game model. By introducing such factors as economic benefits, cognition and government policies, this paper construct an evolutionary game model, which provides a basis for improving the economic benefits of green buildings. The results show that the first factor affecting enterprise decision-making is the incremental profit of green building developers, followed by the government's incentive policy. After the evolution of the market, the final strategic choice will be stabilized to higher economic benefits. Generally speaking, green buildings need to effectively control incremental costs and consider scale benefits. Through management efficiency innovation and policy stimulation, the problems of huge investment cost and long payback period can be solved, so as to improve the economic benefits of green building development. [ABSTRACT FROM AUTHOR]

Industrial cluster is a common phenomenon in the process of industrialization. It is an external scale formed by agglomeration effect. Cluster enterprises based on their comparative advantages, innovation will be in the system innovation, management innovation, technological innovation, corporate culture innovation and other all-round development. The empirical results show that the development of industrial clusters attracts and promotes a large number of intermediary service-oriented organizations, as well as institutions providing research and development and technical support, providing innovative incubation platform. The diffusion behavior of technological innovation of enterprises in clusters plays an important role in upgrading industrial clusters, but uncontrolled imitation behavior will reduce the expected profits of enterprises taking the lead in innovation and increase innovation risks. At the same time, the supporting innovation service system integrates and improves the factors gathering and optimization needed for innovation, and provides a technological support platform for enterprises in clusters to innovate.Therefore, the government's policy should be biased towards establishing an effective mechanism for the interaction between scientific and technological innovation and industrial clusters, so as to achieve a good situation of the interaction between economy and science and technology. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy reinforcement learning (RL)-based tracking control algorithm is first proposed for partially unknown systems with actuator faults. Based on Takagi–Sugeno fuzzy model, a novel fuzzy-augmented tracking dynamic is developed and the overall fuzzy control policy with corresponding performance index is designed, where four kinds of actuator faults, including actuator loss of effectiveness and bias fault, are considered. Combining the RL technique and fuzzy-augmented model, the new fuzzy integral RL-based fault-tolerant control algorithm is designed, and it runs in real time for the system with actuator faults. The dynamic matrices can be partially unknown and the online algorithm requires less information transmissions or computational load along with the learning process. Under the overall fuzzy fault-tolerant policy, the tracking objective is achieved and the stability is proven by Lyapunov theory. Finally, the applications in the single-link robot arm system and the complex pitch-rate control problem of F-16 fighter aircraft demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence (AI) in modeling of various machining processes has been the topic of immense interest among the researchers since several years. In this direction, the principle of fuzzy logic, a paradigm of AI technique, is effectively being utilized to predict various performance measures (responses) and control the parametric settings of those machining processes. This paper presents the application of fuzzy logic to model two non-traditional machining (NTM) processes, i.e. electrical discharge machining (EDM) and electrochemical machining (ECM) processes, while identifying the relationships present between the process parameters and the measured responses. Moreover, the interaction plots which are developed based on the past experimental observations depict the effects of changing values of different process parameters on the measured responses. The predicted response values derived from the developed models are observed to be in close agreement with those as investigated during the past experimental runs. The interaction plots also play significant roles in identifying the optimal parametric combinations so as to achieve the desired responses for the considered NTM processes. [ABSTRACT FROM AUTHOR]

This study applies a fuzzy model to simulate an evolution of price forecasts among market investors based on their own risk perception and other multiple criteria decision. According to this imitation, we design a capital market laboratory experiment on the basis of Smith et al. (Econometrica 56:1119–1151, 1988). The experiment sets two groups of experiments to test the investor's market behaviour under the influence of risk perception. The results show that there is no significant difference in changing prices under the influence of perceived systemic risks and non-systemic risks. However, when market returns become more uncertain, investors' perceptions of risk significantly affect their trading behaviour. Furthermore, we divide the investors' investment behaviour into different investment strategies and observe the trading behaviours of the investors with risk perception. In addition, extended analysis shows that under a risk environment, a decline in asset price is related to the amount of cash and assets held by the investors, and the assets purchased in the previous period has no substantial relationship with the asset price in the last period and the forecast accuracy of the investment asset price in the last period. [ABSTRACT FROM AUTHOR]

Adaptive neuro-fuzzy inference system (ANFIS) is a well-known neuro-fuzzy model for approximating highly complex non-linear systems. ANFIS uses precise fuzzy modelling concept that aims at accuracy of a fuzzy model being designed than on interpretability. But interpretability of a fuzzy system is an equally important aspect of fuzzy modelling as accuracy. So far the research based on ANFIS has been mostly application based due to which the various issues related to the interpretability of ANFIS have not been dealt with. The rule base of ANFIS is typically obtained using data driven clustering algorithms. But this process introduces redundancy in the system in terms of similar fuzzy sets and redundant fuzzy rules which unnecessarily increases system complexity. This in turn reduces both the interpretability as well as generalization capability of ANFIS. Additionally in case of ANFIS, unconstrained gradient descent based learning algorithms are used to fine-tune the membership function parameters which usually result in a rule base with inconsistent, excessively overlapping and indistinguishable membership functions for input variables and thus the interpretability of the final optimized system is not guaranteed. This paper is based on addressing the issue of rule base redundancy in ANFIS to reduce complexity and enforcing constraints during learning phase to ensure interpretability of the final optimized system. Rule base redundancy is removed using similarity analysis based rule base simplification in which similar fuzzy sets are merged and subsequently resulting fuzzy rules with equal premises are combined. Hybrid learning technique which is an efficient parameter tuning method for ANFIS is constrained to prevent inconsistency, excessive overlapping and inclusion of membership functions so that the final fuzzy partitions of inputs stay interpretable. The empirical analysis of the impact of rule base simplification and constrained learning on ANFIS is done by application to two well-known benchmark problems and a real world stock price prediction problem. The introduction of rule base simplification and constrained learning in ANFIS modeling has shown better results in terms of obtaining a desired accuracy-interpretability tradeoff than conventional ANFIS. [ABSTRACT FROM AUTHOR]

Vigilance, also known as sustained attention, is defined as the ability to maintain concentrated attention over prolonged time periods. Many methods for vigilance detection, based on biological and behavioural characteristics, have been proposed in the literature. In general, the existing approaches do not provide any solution to measure vigilance level quantitatively and adopt costly equipment. This paper utilizes a portable electroencephalography (EEG) device and presents a new method for estimation of vigilance level of an individual by utilizing event-related potentials (P300 and N100) of EEG signals and eye blink rate. Here, we propose a fuzzy rule-based system using amplitude and time variations of the N100 and P300 components and blink variability to establish the correlation among N100, P300, eye blink and the vigilance activity. We have shown, with the help of our proposed fuzzy model, we can efficiently calculate and quantify the vigilance level, and thereby obtain a numerical value of vigilance instead of its mere presence or absence. To validate the results obtained from our fuzzy model, we performed subjective analysis (for assessing the mood and stress level of participants), reaction time analysis and compared the vigilance values with target detection accuracy. The obtained results prove the efficacy of our proposal. [ABSTRACT FROM AUTHOR]

Maintenance is essential to ensure safe operation of equipment in normal conditions. Therefore, managers must identify the relative priorities and equipment maintenance requirements. Moreover, based on the results of equipment vulnerability assessments, maintenance programs can be developed and managed properly. There are different methods and techniques in the process of risk assessment and management and vulnerability of equipment. Seventy-six samples with different properties have been used in this study. Networks used in this study are self-organizing networks with constant weight, which include Kohonen networks. For this purpose, operation impact, operation flexibility, maintenance cost, impact of safety and environment and frequency parameters had been considered as input; and using this model, the risk level is calculated. Utilizing genetic algorithms, the structures of all self-organizing systems are optimized. In order to evaluate the accuracy of the model, we compare it with the fuzzy model, and the results indicate that self-organizing systems optimized with the genetic algorithm have higher ability, flexibility and accuracy than the fuzzy model in predicting risk. [ABSTRACT FROM AUTHOR]

The method for portfolio investment, allowing the formation of the optimal portfolio structure considering degrees of satisfaction of requirements of stakeholder groups, risks and uncertainty of external and internal environment, was proposed. The model that represents a fuzzy nonlinear programming problem was considered. The weighted average of project utility is used as objective function. The utilities of projects are multiplicative Cobb-Douglas type functions using, along with financial indicators, expert verbal evaluations of qualitative indicators of satisfaction of stakeholder requirements, converted into fuzzy numbers. Exponents in this function reflect the significance of stakeholders for the organization in terms of the existing resource sharing between a company and a stakeholder and the degree of mutual influence. Quantitative accounting of risks is implemented based on the H. Markowitz approach and the scenario-based method. Uncertainty and lack of information for the indicator of economic efficiency of projects is modeled through the use of the fuzzy approach. Constraints in the model are also fuzzy. The transforming from a fuzzy optimization problem into a crisp problem is performed by assigning the satisfaction degrees for an objective function and the constraints. The choice of a certain satisfaction degree also makes it possible to some extent to take into account uncertainty, which, in turn, affects the composition of the portfolio. The solution to the model is found numerically using the proposed method, which allows, based on fuzzy utilities, finding fuzzy objective function and constraints, and transforming a fuzzy model into a crisp quadratic programming problem at specified satis-faction degrees. The example of the formation of an optimal portfolio of investment projects of a fishing industrial enterprise was exploredc. [ABSTRACT FROM AUTHOR]

The ascent prediction of high-altitude zero-pressure stratospheric balloons is an important aspect of targeted test flight. Prediction of the balloon ascent rate is the prerequisite for many of the flights as it helps in planning ballasting and valving manoeuvres. In this paper, a standard analytical model, a fuzzy model and a statistical regression model are developed and compared to predict the zero-pressure balloon ascent. The flight data is extracted from the Esrange balloon service system for zero-pressure balloons with different payload capability, and several potential explanatory variables are computed for every sampled climbed segment. For the fuzzy modelling approach, a fuzzy c-mean clustering algorithm is used for system identification and prediction. For the regression approach, a Gaussian process regression is used, and principal component analysis is applied for finding the significant inputs. The result shows that the data driven approaches are more efficient than the standard analytical model. [ABSTRACT FROM AUTHOR]