MODELS OF DYNAMIC COMPLEXITY FOR TIME-SERIES PREDICTION
Visakan Kadirkamanathan, Mahesan Niranjan and Frank Fallside
1992
In this paper, we have developed a model of dynamic complexity, a growing Gaussian radial basis function (GRBF) network, by analysing sequential learning in the function space. The criteria to add a new basis function to the model are based on the angle formed between a new basis function and the existing basis functions and also on the prediction error. When a new basis function is not added the model parameters are adapted by the extended Kalman filter (EKF) algorithm. This model is similar to the resource allocating network (RAN) and hence this work provides an alternative interpretation to the RAN. An enhancement to the RAN is suggested where RAN is combined with EKF. The RAN and its variants are applied to the task of predicting the logistic map and the Mackey-Glass chaotic time-series and the advantages of the enhanced model are demonstrated.
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