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Neural Computing and Applications
Published in: Neural Computing and Applications 2/2013

01-08-2013 | Original Article

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

Authors: Chi-Yao Hsu, Sheng-Fuu Lin, Jyun-Wei Chang

Published in: Neural Computing and Applications | Issue 2/2013

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Abstract

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 evolutionary learning algorithm is utilized to evolve neurons. The good combinations of neurons evolved in NULE are reserved for being the initial populations of NWLE. In NWLE, the initial population are mated and mutated to produce new structure of networks. Similar to NULE, the good neurons of evolved network in NWLE are inserted into the NULE. Thus, by interactive two-level evolutions, the neurons and structure of network can be evolved locally and globally, respectively. Simulation results using DMHCCA are reported and compared with other existing models. Application of DMHCCA to a three-dimensional (3D) surface alignment task is also described, and experimental results are presented better performance than other alignment systems.
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Appendix
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Metadata
Title
Data mining–based hierarchical cooperative coevolutionary algorithm for TSK-type neuro-fuzzy networks design
Authors
Chi-Yao Hsu
Sheng-Fuu Lin
Jyun-Wei Chang
Publication date
01-08-2013
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 2/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0943-0

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