title: A Fast Adaptive Tunable RBF Network For Nonstationary Systems.
abstract: This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.
authors.forename: Hao
authors.initials: H
authors.lastname: Chen
authors.forename: Yu
authors.initials: Y
authors.lastname: Gong
authors.forename: Xia
authors.initials: X
authors.lastname: Hong
authors.forename: Sheng
authors.initials: S
authors.lastname: Chen