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

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• 作者：Chi-Yao Hsu (1)
  Sheng-Fuu Lin (1)
  Jyun-Wei Chang (1)
  
• 关键词：Hierarchical cooperative coevolutionary algorithm ; Neuro ; level evolution ; Network ; level evolution ; Data mining–based evolutionary learning algorithm ; Three ; dimensional surface alignment
• 刊名：Neural Computing & Applications
• 出版年：2013
• 出版时间：August 2013
• 年：2013
• 卷：23
• 期：2
• 页码：485-498
• 全文大小：634KB
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• 作者单位：Chi-Yao Hsu (1)
  Sheng-Fuu Lin (1)
  Jyun-Wei Chang (1)
  
  1. Department of Electrical Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, 300, Taiwan, ROC
  

文摘

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.