Online modeling with tunable RBF network

In this paper, we propose a novel online modeling algorithm for nonlinear and nonstationary systems using a radial basis function (RBF) neural network with a fixed number of hidden nodes. Each of the RBF basis functions has a tunable center vector and an adjustable diagonal covariance matrix. A mult...

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Bibliographic Details
Main Authors: Hao Chen, Yu Gong, Xia Hong
Format: Default Article
Published: 2012
Subjects:
Mechanical engineering not elsewhere classified
Artificial intelligence not elsewhere classified
Multi-innovation recursive least square (MRLS)
Nonlinear
Nonstationary
Online modeling
Quantum particle swarm optimization (QPSO)
Radial basis function (RBF)
Mechanical Engineering not elsewhere classified
Artificial Intelligence and Image Processing
Online Access:https://hdl.handle.net/2134/25658
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