On-line system identification using Chebyshev neural networks

This paper proposes a computationally efficient artificial neural network (ANN) model for system identification of unknown dynamic nonlinear continuous and discrete time systems. A single layer functional link ANN is used for the model where the need of hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials. These models are linear in their parameters. The recursive least squares method with forgetting factor is used as on-line learning algorithm for parameter updating. The good behaviour of the identification method is tested on two single input single output (SISO) continuous time plants and two discrete time plants. Stability of the identification scheme is also addressed.