Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks

A computationally efficient artificial neural network (ANN) for the purpose of dynamic nonlinear system identification is proposed. The major drawback of feedforward neural networks such as a multilayer perceptron (MLP) trained with backpropagation (BP) algorithm is that it requires a large amount of computation for learning. We propose a single-layer functional link ANN (FLANN) in which the need of hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials. The novelty of this network is that it requires much less computation than that of a MLP. We have shown its effectiveness in the problem of nonlinear dynamic system identification. In presence of additive Gaussian noise to the plant, the performance of the proposed network is found similar or superior to that of a MLP. Performance comparison in terms of computational complexity has also been carried out.