Non-linear system identification using Bayesian inference

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Abstract
Many real world systems can only be described well by non-linear models. The analysis and use of non-linear models can be very difficult and time consuming. An attractive class of models is one whose analysis can be based directly on linear systems analysis. One such class comprises models that are linear-in-the-parameters. Such models tend to have extremely large numbers of parameters, although only a handful may be relevant for any particular data set. An algorithm is described that enables the important parameters of a model to be found. Bayesian inference is used to give a consistent framework for fitting models and selecting between competing models. Simulations are given illustrating the problems of parameter estimation and model selection.