Intelligent Structure Selection of Polynomial Nonlinear Systems using Evolutionary Programming

The present study proposes an alternate method of structure selection or which terms to include into a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model, based on evolutionary programming (EP). The algorithm uses a strategy similar to elitism where the single best chromosome in a generation is retained and passed to the next. In addition to minimizing the mean square error (MSE), the method introduces an internal term penalty (ITP) function to reject spurious terms under the effects of significant noise. By following an adaptive mutation rate and restricting this to vary within 50%, faster convergence is achieved. To further improve the convergence, a pruning strategy is followed where any insignificant terms are removed from the model by assigning them with a time-to-live parameter. The performance of the proposed method is illustrated considering several examples of nonlinear systems and have been found to be satisfactory