Neural networks for rapid design and analysis

Abstract

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems.Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably.To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

Using neural networks for goal driven simulation

“…These approaches although very valuable gave just partial results; "in the real world no system is analyzed on one variable" (Sevimoglu 2002). Most realistic systems are of high order and/or nonlinear, which precludes a direct solution (Sparks and Maghami 1998).…”