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Nonlinear identification using a B-spline neural network and chaotic immune approaches by Leandro dos Santos Coelho; Marcelo Wicthoff Pessôa is a Engineering article available to read on EtoBox.

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One of the important applications of B-spline neural network (BSNN) is to approximate nonlinear functions defined on a compact subset of a Euclidean space in a highly parallel manner. Recently, BSNN, a type of basis function neural network, has received increasing attention and has been applied in the field of nonlinear identification. BSNNs have the potential to ''learn'' the process model from input-output data or ''learn'' fault knowledge from past experience. BSNN can be used as function approximators to construct the analytical model for residual generation too. However, BSNN is trained by gradient-based methods that may fall into local minima during the learning procedure. When using feed-forward BSNNs, the quality of approximation depends on the control points (knots) placement of spline functions. This paper describes the application of a modified artificial immune network inspired optimization method À the opt-aiNet À combined with sequences generate by He ́non map to provide a stochastic search to adjust the control points of a BSNN. The numerical results presented here indicate that artificial immune network optimization methods are useful for building good BSNN model f

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Author

Leandro dos Santos Coelho; Marcelo Wicthoff Pessôa

Publisher

Elsevier Science; Elsevier ; Elsevier Inc.; Elsevier BV (ISSN 0888-3270)

Published

2009

Language

EN

Field

Engineering (Physical Sciences)