A GA paradigm for learning fuzzy rules

Lim, Meng‐Hiot; Rahardja, Susanto; Gwee, Bah-Hwee

doi:10.1016/0165-0114(95)00254-5

Cited by 72 publications

(39 citation statements)

References 3 publications

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“…It results in performance that may not be optimal [14], [15], [16], [17]. In our work, the RcFIP is configured under the guidance of a genetic algorithm (GA) based evolution module, as shown in Fig.…”

Section: Rcfip_3mentioning

confidence: 99%

Multi-tasking of fuzzy inference processor through real-time context switching

Cao

Song

Shi

et al. 2009

2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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Section: Rcfip_3mentioning

confidence: 99%

Multi-tasking of fuzzy inference processor through real-time context switching

Cao

Song

Shi

et al. 2009

2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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“…For simplicity, the fuzzy rules are expressed as a linguistic matrix (see [38]). In such a linguistic matrix, the left-most column and the first row denote the variables involved in the antecedent part of the rules.…”

Section: Fuzzy Modeling (Process 1)mentioning

confidence: 99%

An investigation of fuzzy multiple heuristic orderings in the construction of university examination timetables

Asmuni

Burke

Garibaldi

et al. 2009

Computers & Operations Research

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In this paper, we present an investigation into using fuzzy methodologies to guide the construction of high quality feasible examination timetabling solutions. The provision of automated solutions to the examination timetabling problem is achieved through a combination of construction and improvement. The enhancement of solutions through the use of techniques such as metaheuristics is, in some cases, dependent on the quality of the solution obtained during the construction process. With a few notable exceptions, recent research has concentrated on the improvement of solutions as opposed to focusing on investigating the 'best' approaches to the construction phase. Addressing this issue, our approach is based on combining multiple criteria in deciding on how the construction phase should proceed. Fuzzy methods were used to combine three single construction heuristics into three different pair wise combinations of heuristics in order to guide the order in which exams were selected to be inserted into the timetable solution. In order to investigate the approach, we compared the performance of the various heuristic approaches with respect to a number of important criteria (overall cost penalty, number of skipped exams, number of iterations of a rescheduling procedure required and computational time) on twelve well-known benchmark problems. We demonstrate that the fuzzy combination of heuristics allows high quality solutions to be constructed. On one of the twelve problems we obtained lower penalty than any previously published constructive method and for all twelve we obtained lower penalty than when any of the single heuristics were used alone. Furthermore, we demonstrate that the fuzzy approach used less backtracking when constructing solutions than any of the single heuristics. We conclude that this novel fuzzy approach is a highly effective method for heuristically constructing solutions and, as such, has particular relevance to real- Preprint submitted to Elsevier 29 April 2008world situations in which the construction of feasible solutions is often a difficult task in its own right.

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“…With this method, only the nonblank cells, instead of all the cells as in [14], are used to form a fuzzy system. Similarly, in [17], a coding method was proposed so that the user can make sure that only rules were selected from the possible rule combinations in a grid partition. The fuzzy rules used in the aforementioned techniques are rules with fuzzy sets in their consequents.…”

mentioning

confidence: 99%

Genetic reinforcement learning through symbiotic evolution for fuzzy controller design

Juang

Lin

2000

IEEE Trans. Syst., Man, Cybern. B

184

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Abstract-An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSK-type fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSK-type fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cart-pole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GA-based fuzzy systems.

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A GA paradigm for learning fuzzy rules

Cited by 72 publications

References 3 publications

Multi-tasking of fuzzy inference processor through real-time context switching

Multi-tasking of fuzzy inference processor through real-time context switching

An investigation of fuzzy multiple heuristic orderings in the construction of university examination timetables

Genetic reinforcement learning through symbiotic evolution for fuzzy controller design

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