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Memristive Chebyshev Neural Network and Its Applications in Function Approximation

Author

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  • Lidan Wang
  • Meitao Duan
  • Shukai Duan

Abstract

A novel Chebyshev neural network combined with memristors is proposed to perform the function approximation. The relationship between memristive conductance and weight update is derived, and the model of a single-input memristive Chebyshev neural network is established. Corresponding BP algorithm and deriving algorithm are introduced to the memristive Chebyshev neural networks. Their advantages include less model complexity, easy convergence of the algorithm, and easy circuit implementation. Through the MATLAB simulation results, we verify the feasibility and effectiveness of the memristive Chebyshev neural networks.

Suggested Citation

  • Lidan Wang & Meitao Duan & Shukai Duan, 2013. "Memristive Chebyshev Neural Network and Its Applications in Function Approximation," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-7, June.
    • Handle: RePEc:hin:jnlmpe:429402
    DOI: 10.1155/2013/429402
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    Cited by:

    1. Chih-Hong Lin, 2020. "Sage Revised Reiterative Even Zernike Polynomials Neural Network Control with Modified Fish School Search Applied in SSCCRIM Impelled System," Mathematics, MDPI, vol. 8(10), pages 1-30, October.

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