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

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...

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Veröffentlicht in:Applied intelligence (Dordrecht, Netherlands) Jg. 36; H. 2; S. 454 - 471
Hauptverfasser: Lin, Sheng-Fuu, Chang, Jyun-Wei, Hsu, Yung-Chi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.03.2012
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Chromosomes
Computer Science
Controllers
Data mining
Evolution
Fuzzy
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Genetic algorithms
Learning
Machines
Manufacturing
Mechanical Engineering
Mutation
Network topologies
Optimization techniques
Processes
Strategy
Identification
Data mining
Genetic algorithm
Fuzzy model
FP-Growth
Group-based symbiotic evolution
ISSN:0924-669X, 1573-7497
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Zusammenfassung: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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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-010-0271-y