Models of dynamic complexity for time-series prediction (neural networks)

A model of dynamic complexity, a growing Gaussian radial basis function (GRBF) network, is developed by analyzing sequential learning in the function space. The criteria for adding 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.<<ETX>>

[1]  Visakan Kadirkamanathan,et al.  Nonlinear adaptive filtering in nonstationary environments , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[2]  John C. Platt A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.

[3]  Visakan Kadirkamanathan,et al.  A nonlinear model for time series prediction and signal interpolation , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.