Chebyschev functional link artificial neural networks for nonlinear dynamic system identification
An alternative novel artificial neural network (ANN) for the purpose of dynamic nonlinear system identification is proposed. The main drawback of feedforward neural networks such as a multi-layer perceptron (MLP) trained with backpropagation (BP) algorithm is that it requires a large amount of computation and the rate of error convergence is slow. The proposed Chebyschev functional link ANN (C-FLANN) is found to have much less computational requirement and its performance is found to be superior to that of a MLP for the complex task of nonlinear dynamic system identification, even in the case of additive input noise to the system.