Neural input selection—A fast model-based approach

Abstract

Neural input selection is an important stage in neural network configuration. For neural modeling and control of nonlinear dynamic systems, the inputs to the neural networks may include any system variable of interest with various time lags. To choose a set of significant inputs is a combinational problem, and the selection procedure can be very time consuming. In this paper, a model-based neural input selection method is proposed. Essentially, the neural input selection is transformed into the problem of identifying the significant terms for a linear-in-the-parameters model. A fast method is then proposed to identify significant nonlinear terms or functions, from which the neural inputs are grouped and selected. Both theoretic analysis and simulation examples demonstrate the effectiveness and efficiency of the proposed model-based approach.