Sliding mode algorithm for online learning in analog multilayer feedforward neural networks

Abstract

A new dynamical sliding mode control algorithm is proposed for robust adaptive learning in analog multilayer feedforward networks with a scalar output. These type neural structures are widely used for modeling, identification and control of nonlinear dynamical systems. The zero level set of the learning error variable is considered as a sliding surface in the space of network learning parameters. The convergence of the algorithm is established and conditions are given. Its effectiveness is shown when applied to on-line learning of non-monotonic function using a two-layered feedforward neural network. © Springer-Verlag Berlin Heidelberg 2003.