Use of artificial neural networks for engineering analysis of complex physical systems

Mathematical models of physical systems are used, among other purposes, to improve our understanding of the behavior of physical systems, predict physical system responses, and control the responses of systems. Phenomenological models are frequently used to simulate system behavior, but an alternative is available-the artificial neural network (ANN). We demonstrate that a particular ANN, the connectionist normalized linear spline (CNLS) network, can be used to simulate mechanical system behavior accurately and efficiently. The system simulations are performed in the non recurrent and recurrent frameworks. The paper provides brief discussions on the operation of ANNs in general and the CNLS net in particular, the operation of two different types of mechanical systems, and approaches to the solution of some special problems that occur in connection with ANN simulation of physical system response. Numerical examples are presented