Sequential learning algorithm for PG-RBF network using regression weights for time series prediction
We propose a modified radial basis function network (RBF) in which the main characteristics are that: a) the gaussian function is modified using pseudo-gaussian (PG) in which two scaling parameters σ are introduced; b) the activation of the hidden neurons is normalized c) instead of using a single parameter for the output weights, these are functions of the input variables; d) a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit and also to detect and remove inactive units. It is shown that the modified PG-RBF can reduce the number of didden units significantly compared with the classical RBF network. The feasibility of the resulting algorithm for the neural network to evolve and learn is demonstrated by predicting time series.
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