A fast input selection algorithm for neural modeling of nonlinear dynamic systems

Abstract

In neural modeling of non-linear dynamic systems, the neural inputs can include any system variable with time delays. To obtain the optimal subset of inputs regarding a performance measure is a combinational problem, and the selection process can be very time-consuming. In this paper, neural input selection is transformed into a model selection problem and a new fast input selection method is used. This method is then applied to the neural modeling of a continuous stirring tank reactor (CSTR) to confirm its effectiveness. © Springer-Verlag Berlin Heidelberg 2005.