標題: Differential evolution with local information for neuro-fuzzy systems optimisation
作者: Han, Ming-Feng
Lin, Chin-Teng
Chang, Jyh-Yeong
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: Neuro-fuzzy systems (NFSs);Differential evolution (DE);Neuro-fuzzy systems optimisation;Evolutionary algorithm (EA);Optimisation
公開日期: 1-May-2013
摘要: This paper proposes a differential evolution with local information (DELI) algorithm for Takagi-Sugeno-Kang-type (TSK-type) neuro-fuzzy systems (NFSs) optimisation. The DELI algorithm uses a modified mutation operation that considers a neighbourhood relationship for each individual to maintain the diversity of the population and to increase the search capability. This paper also proposes an adaptive fuzzy c-means method for determining the number of rules and for identifying suitable initial parameters for the rules. Initially, there are no rules in the NFS model; the rules are automatically generated by the fuzzy measure and the fuzzy c-means method. Until the firing strengths of all of the training patterns satisfy a pre-specified threshold, the process of rule generation is terminated. Subsequently, the DELI algorithm optimises all of the free parameters for NFSs design. To enhance the performance of the DELI algorithm, an adaptive parameter tuning based on the 1/5th rule is used for the tuning scale factor F. The 1/5th rule dynamically adjusts the tuning scale factor in each period to enhance the search capability of the DELI algorithm. Finally, the proposed NFS with DELI model (NFS-DELI) is applied to nonlinear control and prediction problems. The results of this paper demonstrate the effectiveness of the proposed NFS-DELI model. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.knosys.2013.01.023
http://hdl.handle.net/11536/21920
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2013.01.023
期刊: KNOWLEDGE-BASED SYSTEMS
Volume: 44
Issue: 
起始頁: 78
結束頁: 89
Appears in Collections:Articles


Files in This Item:

  1. 000318326800008.pdf