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Mathematical Problems in Engineering
Volume 2013, Article ID 429402, 7 pages
Research Article

Memristive Chebyshev Neural Network and Its Applications in Function Approximation

School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China

Received 1 February 2013; Accepted 22 April 2013

Academic Editor: Chuandong Li

Copyright © 2013 Lidan Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.