Abstract
B-spline neural network (BSNN), a type of basis function neural network, is trained by gradient-based methods, which may fall into local minimum during the learning procedure. To overcome the problems encountered by the conventional learning methods, differential evolution (DE) ( an evolutionary computation methodology ( can provide a stochastic search to adjust the control points of a BSNN are proposed. DE incorporates an efficient way of self-adapting mutation using small populations. The potentialities of DE are its simple structure, easy use, convergence property, quality of solution and robustness. In this paper, we propose a modified DE using chaotic sequence based on logistic map to train a BSNN. The numerical results presented here indicate that the chaotic DE is effective in building a good BSNN model for nonlinear identification of an experimental nonlinear yo-yo motion control system.
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dos Santos Coelho, L., Guerra, F.A. (2007). Identification of an Experimental Process by B-Spline Neural Network Using Improved Differential Evolution Training. In: Saad, A., Dahal, K., Sarfraz, M., Roy, R. (eds) Soft Computing in Industrial Applications. Advances in Soft Computing, vol 39. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70706-6_7
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DOI: https://doi.org/10.1007/978-3-540-70706-6_7
Publisher Name: Springer, Berlin, Heidelberg
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