Rule Base Of Fuzzy System Research Articles

Information Technology for Designing Rule bases of Fuzzy Systems using Ant Colony Optimization

This paper proposes the universal information technology for designing the rule bases (RB) with the formation of optimal consequents for fuzzy systems (FS) of different types on the basis of ant colony optimization (ACO) techniques. The developed ACO-based information technology allows effectively synthesizing rule bases of various dimensions both for the MISO and MIMO fuzzy systems taking into account the particular features of the RB consequents formation in the conditions of insufficient initial information. In order to study and validate the efficiency of the presented information technology the design of the RB for the adaptive fuzzy control system of the ship steering device is carried out in this work. The computer simulations results show that adaptive control system with developed RB provides achievement of high enough quality indicators of rudder angle control. Thus, application of the proposed ACO-based information technology allows designing effective RB with optimal consequents by means of minor computational costs that, in turn, confirms its high efficiency.

Z-Hesitant Fuzzy Network Model with Reliability and Transparency of Information for Decision Systems

Decision-making environment often encounters complexity along its processes, especially in the context of multidisciplinary scientific research. This can commonly be seen in engineering, computing, finance, astrology and other different areas. It is of great restriction in dealing with the practical problems which have diverse demands and properties. There is a growing body of literature that recognizes the importance of dealing with the complexity in decision making environment. The reliability and the transparency are the dominant feature of the integration of fuzzy network and Z-numbers. However, much of the research up to now has been descriptive in nature of the features. Hence, this proposed method is unique and novel because it offers some interesting insight of dealing with reliability and transparency of information in Z-hesitant fuzzy network decision-making environment. The fuzzy networks have the functionality under rule bases of fuzzy systems where it is recognized by its transparency and precision. The proposed method makes use of fuzzy network with the incorporation of hesitant fuzzy sets to assimilate decision information towards alternatives. For the validation and applicability purposes of the proposed method, the case study of stock evaluation assessed by a number of decision makers has been utilized as a real-world problem. The performance of the proposed method is evaluated respectively by applying the Spearman’s rho correlation. The result shows that the proposed method performs as the established method with the consideration of additional dominant features.

Generation of Rule Bases of Fuzzy Systems Based on Modified Ant Colony Algorithms

This article is dedicated to the development and study of the method of generation of fuzzy systems rule bases of Mamdani-type with the formation of optimal consequents based on modified ant colony optimization algorithms. The obtained method makes it possible to effectively generate the rule bases with the optimal consequents for the Mamdani-type fuzzy systems in the following cases: at insufficient amount of initial information (under conditions of a high degree of information uncertainty); at a sufficiently large number of rules for which the development of a rule base based on the knowledge of experts is not always effective; at different levels of experts qualification. The study of the effectiveness of the proposed method in this work is carried out at the development of the rule base of the Mamdani-type fuzzy controller for the automatic control system of the reactor temperature of the specialized pyrolysis complex, that is intended for utilization of polymer waste. At the same time, the generation of the consequents of the rule base of this fuzzy controller was carried out with various adjustable parameters of the method, such as the total number of agents and the number of elite agents in the population. Based on the analysis of the results of the conducted experiments, the optimal configuration of the population of agents of the method was determined, for which the optimal vector of the consequents of the rule base can be found at the lowest computational and time costs. Also, the results of computer simulations of transients showed that the automatic temperature control system with the developed fuzzy controller rule base based on the proposed by the authors method with the optimal configuration of parameters has a significantly lower value of the objective function and higher control quality parameters than the system with the rule base on the basis of experts knowledge, which, in turn, confirms the high efficiency of this method.

FEWER HYPER-ELLIPSOIDS FUZZY RULES GENERATION USING EVOLUTIONAL LEARNING SCHEME

Fuzzy rules generation is known an important task in designing fuzzy systems. This article applies an evolutionary fuzzy rules learning scheme to approach desired fuzzy systems having a lower fuzzy rules. The proposed learning scheme overcomes limitations of conventional fuzzy rules generation and completes the complex searching problems to extract the desired fuzzy system. In this article, aggregations of hyper-ellipsoids fuzzy partitions with different sizes and different positions are suggested to approximate the knowledge rule base of fuzzy systems whose membership functions are arbitrarily shaped and flexibly tuned in parameters searching space. Several corresponding parameters in defining the region of such hyper-ellipsoids type membership functions are efficiently selected based on the simple rule extracting technology. Furthermore, the constructed fuzzy system with only two fuzzy rules can be automatically extracted by the evolutional genetic algorithms (GAs) learning scheme with the guide of special fitness function. Finally, both inverted pendulum balance and nonlinear modeling problems are used to illustrate the effectiveness of the proposed method.