Abstract
A localised forgetting method is proposed for on-line adaptation of Gaussion Radial Basis Function Network (RBFN) models. It is realised that the typically used forgetting factor is uniformly applied to the past data in entire operating space and is not correct for nonlinear systems where dynamics are different in different operating regions. The new method sets different regions with different forgetting factor according to the response of the local centre to the current measurement data. The method is based on the Recursive Orthogonal Least Squares (ROLS) algorithm and is simple. Application of the new method to the modelling of dissolved oxygen in a chemical reactor rig shows a smaller mean squared error (MSE) for one-step-ahead prediction than using the uniform forgetting, and indicates the effectiveness of the method.
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