Fuzzy-Genetic Algorithm for Pre-Processing Data at the RTU

Kumar, Pragati; Chandna, Vinay Kumar; Thomas, Mini S.

doi:10.1109/tpwrs.2004.825924

Cited by 22 publications

References 16 publications

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A self-organization mining based hybrid evolution learning for TSK-type fuzzy model design

2010

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In this paper, a self-organization mining based hybrid evolution (SOME) learning algorithm for designing a TSK-type fuzzy model (TFM) is proposed. In the proposed SOME, group-based symbiotic evolution (GSE) is adopted in which each group in the GSE represents a collection of only one fuzzy rule. The proposed SOME consists of structure learning and parameter learning. In structure learning, the proposed SOME uses a two-step self-organization algorithm to decide the suitable number of rules in a TFM. In parameter learning, the proposed SOME uses the data mining based selection strategy and data mining based crossover strategy to decide groups and parental groups by the data mining algorithm that called frequent pattern growth. Illustrative examples were conducted to verify the performance and applicability of the proposed SOME method.

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A self-organization mining based hybrid evolution learning for TSK-type fuzzy model design

2010

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Fuzzy adaptive genetic algorithm based on auto-regulating fuzzy rules

Su-qiong

2010

J. Cent. South Univ. Technol.

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There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms (SGA) as the values of crossover probability (P c ) and mutation probability (P m ) are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm (FAGA) based on the auto-regulating fuzzy rules (ARFR-FAGA). By using the fuzzy control method, the values of P c and P m were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm.Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise (EFAGA) obviously in the global search.

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An efficient evolutionary algorithm for fuzzy inference systems

2010

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In this paper, a novel self-constructing evolutionary algorithm (SCEA) for designing a TSK-type fuzzy model (TFM) is proposed. The proposed SCEA method is different from normal genetic algorithms (GAs). A chromosome of a population in traditional GAs represents a full solution and only one population presents all solutions in each generation. Our proposed method uses a population to evaluate a partial solution locally and applies several populations to construct a full solution. Thus, a chromosome represents only a partial solution. The proposed SCEA method uses the self-constructing learning algorithm to construct the TFM automatically that is based on the input training data to decide on the input partition. Fuzzy rules are created and begin to grow as the first training pattern arrives. Thus, the user need not give any a priori knowledge or even any initial information on the SCEA. We also adopted the sequence search-based dynamic evolution (SSDE) method to carry out parameter learning of the TFM. Simulation results have shown that the proposed SCEA method performs better than some existing methods.

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Fuzzy-Genetic Algorithm for Pre-Processing Data at the RTU

Cited by 22 publications

References 16 publications

A self-organization mining based hybrid evolution learning for TSK-type fuzzy model design

A self-organization mining based hybrid evolution learning for TSK-type fuzzy model design

Fuzzy adaptive genetic algorithm based on auto-regulating fuzzy rules

An efficient evolutionary algorithm for fuzzy inference systems

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