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

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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.

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APA

Shakev, N. G., Topalov, A. V., & Kaynak, O. (2003). Sliding mode algorithm for online learning in analog multilayer feedforward neural networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2714, 1064–1072. https://doi.org/10.1007/3-540-44989-2_127

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