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 min-
imum problem, this paper alternatively proposes to apply harmony search algorithm to find optimal network parame- ters, so as to achieve better performances of FCMAC. The harmony search algorithm optimizes not only FCMAC net- work’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 algo- rithm. In particular, the self-adaptive global best harmony
search algorithm (SGHS) is used to search optimal parame- ter 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 opti- mized by SGHS perform the faster convergence speed and better accuracy.

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