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This paper introduces an Expert System acting as an intelligent agent to interrogate past aircraft occurrence data to support the aircraft occurrence investigators. This Expert System can potentially identify similar occurrences that provide indicators of contributing factors that may otherwise have been missed. This intelligence comes from the Expert System's ability to learn from past occurrence data using the combined Fuzzy Logic System (FLS) inference technique and AcciMaps to emulate the aircraft occurrence investigators' evidence analytical reason model. The learned expert knowledge is presented graphically and interactively to the user (investigator) using the AcciMaps. The paper includes an overview of the Australian Transport Safety Bureau (ATSB) investigation evidence analysis workflow, popular mainstream inference techniques used in Expert Systems, evaluation of the proposed Fuzzy Logic System (FLS) inference technique and AcciMap, including an example study. The FLS inference technique has met the Expert System requirements to support aviation occurrence investigations. [ABSTRACT FROM AUTHOR]

• Bibliometric study on fuzzy systems research. • A combined methodology designed to maximize accurate results. • Novel visualizations of results, based on big data processing software. • Expert systems with Applications as a top fuzzy systems research jorunal. Fuzzy systems research (FSR) is a continuously growing academic field which developments and applications extend to a wide-ranging variety of scientific areas. Academic journals play a decisive role in the scientific communication process and the dissemination of novel ideas. The current developments in information technologies bring both challenges and opportunities for scientific journals to analyze, debate, and share scientific findings. Applying bibliometric and network visualization techniques, this paper seeks to analyze the evolution of the FSR field based on journals from 1965 to 2019. The aim is to visualize the current landscape of key highly oriented FSR journals, focusing on productivity, influence, and synergy. The paper uses data retrieved from the Web of Science scientific database and uses a combined methodology specifically designed to maximize the retrieval of significant documents. Results show that the journals with the highest productivity analyzed by period are Mathematical Analysis and Applications (1965–1979), Fuzzy Sets and Systems [FSS](1980–2009), and the Journal of Intelligent Fuzzy Systems (2010–2019). The most cited journals of the last 20 years are FSS, IEEE Transactions on Fuzzy Systems, and IEEE Transactions on Systems Man Cybernetics Systems, while the most relevant for impact factor is Information Fusion. Moreover, a bibliographic coupling analysis by decades is proposed, showing not only the natural impact and relation that the interaction between journals generate but the impact that the rapid development in communications has in the diversification and dissemination of influence, citations, and productivity of scientific journals. [ABSTRACT FROM AUTHOR]

This paper presents the properties, identification issues and utilisation of a new concept of probabilistic fuzzy system for the innovative project risk assessment. This system constitutes the extension of Mamdani probabilistic fuzzy system. For this purpose, a group of risk factors, which influence risk variables, has been chosen. Linguistic risk variables are inputs to the innovation risk assessment system. The structure of fuzzy sets for linguistic values takes into account knowledge of a number of experts. Knowledge is presented as fuzzy IF–THEN rules together with probability measures of fuzzy events occurrence in the antecedent and conclusion of rules. The paper presents a new method of identification of the analysed system. The method uses parametric family of triangular t-norms, which facilitates inference parameters optimisation, enables flexible adjustment of a system to empirical data and makes the system more precise. The modified FP-Growth algorithm to create probabilistic fuzzy rule base is used. Using assumption of the minimal support of rules enables decreasing of knowledge base complexity while preserving the level of identification quality, comparable to the system with full marginal and conditional probability distributions. The results of the system inference have been compared with regression model and Mamdani fuzzy inference system. Finally, the numerical experiments show more precision of system inference than the compared method. The example of analytical use of created probabilistic fuzzy knowledge base in the context of technical innovation risk assessment is also presented. The constructed expert system has an identification character and it can be develop as a tool to help the assessment of applications for funding the implementation of innovative projects by the institutions established for this purpose. [ABSTRACT FROM AUTHOR]

• An approach to prioritizing the customers is proposed. • The approach includes a two–stage fuzzy inference system. • Four criteria and arrival time is used for prioritization. • The results show fair prioritization with lower waiting times. This paper presents a fuzzy expert system for prioritization of heterogeneous customers with different needs. In this way, it is possible to improve the average waiting cost of a queuing system and enhance customer satisfaction as the basic component of organizations. Each customer has his own specific characteristics and demands and expects the system to understand his value and provide the best service. In this study, a two-stage Mamdani fuzzy inference system (FIS) was applied as a decision-making approach to prioritize customers based on Service Duration, Service Value, Customer Loyalty, Maximum Toleration, and Waiting Time. For this purpose, an expert system was developed that can be applied in various fields perfectly. To evaluate and analyze the performance of Fuzzy Prioritization System (FPS), the proposed model is compared with the First-In-First-Out (FIFO) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods as common approaches for prioritizing customers. The results of numerical experiments revealed the efficiency of the proposed system compared to these two methods. [ABSTRACT FROM AUTHOR]

It is well known the fact that the design of a fuzzy control system is based on the human expert experience and control engineer knowledge regarding the controlled plant behavior. As a direct consequence, a fuzzy control system can be considered as belonging to the class of intelligent expert systems. The tuning procedure of a fuzzy controller represents a quite difficult and meticulous task, being based on prior data regarding good knowledge of the controlled plant. The complexity of the tuning procedure increases with the number of the fuzzy linguistic variables and, consequently, of the fuzzy inference rules and thus, the tuning process becomes more difficult. The paper presents a new design strategy for such expert fuzzy system, which improves their performance without increasing the number of fuzzy linguistic variables. The novelty consists in extending the classic structure of the fuzzy inference core with an intelligent module, which tunes one of the control singletons, providing a significant simplification of the design and implementation procedure. The proposed strategy implements a logical, not physical, supplementation of the linguistic terms associated to the controller output. Therefore, a fuzzy rules set with a reduced number of linguistic terms is used to implement the expert control system. This logical supplementation is based on an intelligent algorithm which performs a shifting of only one of the control singletons (the singleton associated to the SMALL_ linguistic variable), its value becoming variable, a fact that allows an accurate control and a better performance for the expert control system. The logic of this intelligent algorithm is to initially provide a high controller output, followed by a slowdown of the control signal near to the operating set point. The main advantage of the proposed expert control strategy is its simplicity: a reduced number of linguistic terms, combined with an intelligent tuning of a single parameter, can provide results as accurate as other more complex available solutions involving tuning of several parameters (well described by the technical literature). Also, a simplification of the preliminary off-line tuning procedure is performed by using a reduced set of fuzzy rules. The generality of the proposed expert control strategy allows its use for any other controlled process. [ABSTRACT FROM AUTHOR]

Decision on alternative-fuel vehicles is one of the most important problems for fleet operations. In this paper we propose a hierarchical hesitant fuzzy linguistic model that captures hesitant linguistic evaluations of multiple experts on multiple criteria for alternative-fuel vehicles. We apply the proposed model on the alternative-fuel vehicle selection problem of a home health care service provider in the USA. The results show that an electric vehicle is the best fit for the application in today’s conditions. We also show robustness of the decision through a sensitivity analysis as well as analyze three scenarios representing possible changes in conditions. [ABSTRACT FROM AUTHOR]

WiMAX is a futuristic technology which provides simultaneous support for web, video, and voice applications. WiMAX networks are best suitable to real time traffic however the quantity of non real time and best effort traffic cannot be neglected. Distribution of resources in such heterogeneous applications is therefore a challenging task. There are many schedulers available for WiMAX but adaptive and adequate schedulers are still in growing stage of development. This paper introduces a novel method using which a system is developed based on concepts of fuzzy logic to schedule traffic in WiMAX networks. The proposed fuzzy expert system simplifies fair allocation of resources to real as well as non real time traffic. The implementation is based on changing the weights of the queues serving real and non- real time traffic adaptively. New weights will be calculated for each bandwidth request made to base station and these weights will in turn decide amount of bandwidth allocated to different traffic classes. The weights are calculated based on three parameters that are amount of real time and non real time traffic in queues, change in throughput requirement for non real time flows and latency requirement of real time input data. Results obtained by virtue of simulations justify the significance of the proposed method. [ABSTRACT FROM AUTHOR]