Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
IEEE Transactions on Signal Processing
The Chebyshev-polynomials-based unified model neural networks forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of nonlinear dynamic systems using functional linkartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear dynamic system identification using Chebyshev functionallink artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, an efficient nonlinear artificial neural network (ANN) equalizer structure for channel equalization is proposed. The network structure utilizes expanding the input vector into the functional expansion block using expanded Chebyshev polynomials to form an enhanced high dimensional space. The structure of the Chebyshev neural network (ChNN) is used for equalization of linear and nonlinear channels. The Performance comparison of the ChNN has been carried out through extensive computer simulations and other neural networks equalizers. The result compared to show that ChNN provides superior performance in terms of convergence rate, computational complexity, MSE floor and BER over a wide range of channel conditions.