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Adaptive sliding mode approach for learning in a feedforward neural network

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Abstract

An adaptive learning algorithm is proposed for a feedforward neural network. The design principle is based on the sliding mode concept. Unlike the existing algorithms, the adaptive learning algorithm developed does not require a prioriknowledge of upper bounds of bounded signals. The convergence of the algorithm is established and conditions given. Simulations are presented to show the effectiveness of the algorithm.

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Authors and Affiliations

  1. Central Queensland University, Faculty of Informatics and Communication, 4702, Rockhampton, QLD, Australia

    X. Yu

  2. Department of Electrical Engineering and Computer Science, University of Tasmania, Hobart, Australia

    M. Zhihong

  3. Department of Computer Science and Software Engineering, Monash University, Caulfield Campus, Australia

    S. M. Monzurur Rahman

Authors
  1. X. Yu
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  2. M. Zhihong
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  3. S. M. Monzurur Rahman
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Yu, X., Zhihong, M. & Rahman, S.M.M. Adaptive sliding mode approach for learning in a feedforward neural network. Neural Comput & Applic 7, 289–294 (1998). https://doi.org/10.1007/BF01428120

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  • DOI: https://doi.org/10.1007/BF01428120

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