Neural Networks for System Identification

Abstract

Recent advances in the software and hardware technologies of neural networks have motivated new studies in architecture and applications of these networks. Neural networks have potentially powerful characteristics which can be utilized in the development of our research goal, namely, a true autonomous machine. Machine learning is a major step in this development. This paper presents the results of our recent study on neural-network-based machine learning. Two approaches for learning and identification of dynamical systems are presented. A Hopfield network is used in a new identification structure for learning of time varying and time invariant systems. This time domain approach results in system parameters in terms of activation levels of the network neurons. The second technique, which is in frequency domain, utilizes a set of orthogonal basis functions and Fourier analysis network to construct a dynamic system in terms of its Fourier coefficients. Mathematical formulations of each technique and simulation results of the networks are presented.