Abstract
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.
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