Data mining–based hierarchical cooperative coevolutionary algorithm for TSK-type neuro-fuzzy networks design

This study proposes a data mining–based hierarchical cooperative coevolutionary algorithm (DMHCCA) for TSK-type neuro-fuzzy networks design. The proposed DMHCCA consists of two-level evolutions: the neuro-level evolution (NULE) and the network-level evolution (NWLE). In NULE, a data mining–based evo...

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Veröffentlicht in:	Neural computing & applications Jg. 23; H. 2; S. 485 - 498
Hauptverfasser:	Hsu, Chi-Yao, Lin, Sheng-Fuu, Chang, Jyun-Wei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.08.2013 Springer
Schlagworte:	Applied sciences Artificial Intelligence Combinatorics Combinatorics. Ordered structures Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer science; control theory; systems Data Mining and Knowledge Discovery Designs and configurations Exact sciences and technology Experimental design Image Processing and Computer Vision Learning and adaptive systems Mathematics Original Article Probability and statistics Probability and Statistics in Computer Science Sciences and techniques of general use Statistics Network-level evolution Data mining–based evolutionary learning algorithm Hierarchical cooperative coevolutionary algorithm Neuro-level evolution Three-dimensional surface alignment Network structure Neural computation Three-dimensional calculations Evolutionary algorithm Neural network Data mining Surface Alignment Experimental result Neuron Data mining―based evolutionary learning algorithm System performance Experimental design Learning algorithm
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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