Title :
Models of dynamic complexity for time-series prediction [neural networks]
Author :
Kadirkamanathan, Visakan ; Niranjan, Mahesan ; Fallside, Frank
Author_Institution :
Dept. of Eng., Cambridge Univ., UK
Abstract :
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
Keywords :
Kalman filters; filtering and prediction theory; learning (artificial intelligence); neural nets; time series; Gaussian radial basis function network; Mackey-Glass chaotic time-series; dynamic complexity; extended Kalman filter; function space; logistic map; model; neural networks; prediction error; resource allocating network; sequential learning; time-series prediction; Artificial neural networks; Chaos; Filters; Glass; Logistics; Neural networks; Predictive models; Radio access networks; Resource management;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0532-9
DOI :
10.1109/ICASSP.1992.226068
