Application of a new criterion for modeling systems

A new criterion is proposed for modeling systems which promises to be useful in deciding how complex a model should be. The criterion is based on the expected model response error instead of the error in fitting the data used for estimating the model parameters. The new criterion also does not require withholding data to be used exclusively for testing. There remains, however, the difficulty of testing a large number of candidate models that correspond to the combinations of terms used in the dynamic equations. A computational approach is suggested which greatly reduces the computations required in searching for the best model. In the suggested approach the gradient of the response with respect to the model coefficients is held fixed and numerous combinations of terms are assessed. After determining the most promising candidate model, the gradient is updated and the process is repeated. This procedure gives greater assurance that the best model is selected and does not rely on the analyst's judgement.