A fast adaptive tunable RBF network for nonstationary systems

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 res...

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Bibliographic Details

Main Authors:	Hao Chen, Yu Gong, Xia Hong, Sheng Chen
Format:	Default Article
Published:	2015
Subjects:	Mechanical engineering not elsewhere classified Artificial intelligence not elsewhere classified Radial basis function (RBF) Multi-innovation recursive least square (MRLS) Nonlinear Nonstationary On-line identification Mechanical Engineering not elsewhere classified Artificial Intelligence and Image Processing
Online Access:	https://hdl.handle.net/2134/23648
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