Adaptive model selection using orthogonal least squares methods
Jaroslav Stark · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 1997
Least squares techniques are commonly used to estimate model parameters from observed data. Often, such data do not become available all at the same time but is rather the result of successive measurements at periodic intervals. In such a case, it is desirable to have methods for updating the parameter estimates using new data as it becomes available. This is particularly important if the underlying model is not stationary. Many algorithms for performing such recursive estimation exist and are widely used in many branches of adaptive signal processing and control theory. At the same time, especially if the model is nonlinear, the number of potential parameters can be extremely large and it is necessary to have some means of picking only the most significant parameters, and discarding the rest. Within the last decade efficient procedures for carrying out such model selection have been developed and are increasingly being used in nonlinear system identification. They have also been applied to a certain class of neural networks to minimize the number of neurons required to accurately model a given problem. The aim of this paper is to combine the above two techniques in order to give an algorithm which can adaptively adjust its choice of model parameters in the light of new data. An example of the application of this algorithm to the adaptive selection of radial basis centres in the prediction of chaotic time series is given.