Nonlinear System Identification Based on Delta-Learning Rules

  • Xin Tan
  • Yong Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


The neural network can be used to identify unknown systems. A novel method based on delta-learning rules to identify the nonlinear system is proposed. First, a single-input-single-output (SISO) discrete-time nonlinear system is introduced, and Gaussian basis functions are used to represent the nonlinear functions of this system. Then the adjustable parameters of Gaussian basis functions are optimized by using delta-learning rules. In the end, simulation results are illustrated to demonstrate the effectiveness of the proposed method.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xin Tan
    • 1
  • Yong Wang
    • 2
  1. 1.School of CommunicationChongqing University of Posts and TelecommunicationsChongqingChina
  2. 2.Department of ComputerChongqing Education CollegeChongqingChina

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