Soft Computing

, Volume 22, Issue 10, pp 3141–3153 | Cite as

Fuzzy cerebellar model articulation controller network optimization via self-adaptive global best harmony search algorithm

  • Fei Chao
  • Dajun Zhou
  • Chih-Min Lin
  • Changle Zhou
  • Minghui Shi
  • Dazhen Lin


Fuzzy cerebellar model articulation controller (FCMAC) networks with excellent nonlinear appropriation ability and simple implementation are used to solve complex uncertainties problems in engineering applications. Both online and off-line learning algorithm of FCMAC networks usually applies the gradient-descent-type methods. However, such gradient-descent methods lead to the high possibility to converging into local minima. To cope with the local minimum problem, this paper alternatively proposes to apply harmony search algorithm to find optimal network parameters, so as to achieve better performances of FCMAC. The harmony search algorithm optimizes not only FCMAC network’s weight variables, but also optimizes network receptive field’s center position and standard deviation parameters. In order to obtain an optimal network, the weight values, center positions, and standard deviations are transformed to three data strings that can be processed by harmony search algorithm. In particular, the self-adaptive global best harmony search algorithm (SGHS) is used to search optimal parameter combinations of FCMAC within solution domains. The network’s performances are verified by approximating six nonlinear formulae. In order to compare the performances of the FCMAC networks optimized by the SGHS algorithm, a back-propagation trained network and another harmony search variant optimized networks are also tested in this work. The experimental results show that the networks optimized by SGHS perform the faster convergence speed and better accuracy.


Neural network optimization Fuzzy CMAC Self-adaptive global best harmony search algorithm 



The authors would like to thank the reviewers for their invaluable comments and suggestions, which greatly helped to improve the presentation of this paper. This work was supported by the Major State Basic Research Development Program of China (973 Program) (No. 2013CB329502), the Fundamental Research Funds for the Central Universities (No. 20720160126), the National Natural Science Foundation of China (Nos. 61673322 and 61673326), and Natural Science Foundation of Fujian Province of China (No. 2017J01128 and 2017J01129).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Cognitive Science Department, Fujian Key Laboratory of Brain-Inspired Computing Technique and Applications, School of InformaticsXiamen UniversityXiamenChina
  2. 2.Department of Electrical EngineeringYuan Ze UniversityChung-Li, TaoyuanTaiwan

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