A Localized Forgetting Method for Gaussian RBFN Model Adaptation
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In this paper a localized forgetting method is proposed for on-line adaptation of Gaussian radial basis function network models. It is realised that the commonly used exponential forgetting applies to the past data from the entire operating space uniformly and therefore, is not correct for nonlinear systems where dynamics are different in different operating regions. The new method proposed in this paper sets different forgetting factors in different regions according to the response of the local centre to the current measurement data. The method is applied in conjunction with the recursive orthogonal Least Squares algorithm and the computing is consequently very efficient. The developed method is applied to modelling of dissolved oxygen in a chemical reactor rig. It shows a smaller mean squared error for one-step-ahead prediction than using the uniform forgetting.
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