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A Joint Stochastic Gradient Algorithm and Its Application to System Identification with RBF Networks

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4 Author(s)
Badong Chen ; State Key Lab. of Intelligent Technol. & Syst., Tsinghua Univ., Beijing ; Jinchun Hu ; Hongbo Li ; Zengqi Sun

Mean-square-error (MSE) and minimum-error-entropy (MEE) criteria play significant roles in adaptive filtering and learning theory. Nevertheless, both the criteria have their respective shortcomings. In this paper, we propose a more general and effective stochastic gradient algorithm under joint criterion of MSE and MEE, and derive the approximate upper bound for the step size in the adaptive linear neuron (ADALINE) training. In particular, we demonstrate the superiority of this joint adaptive algorithm by applying it into system identification with radial basis function (RBF) networks

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Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on  (Volume:1 )

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