Memristive Chebyshev Neural Network and Its Applications in Function Approximation

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.

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