Fast and Efficient Second-Order Method for Training Radial Basis Function Networks

This paper proposes an improved second order (ISO) algorithm for training radial basis function (RBF) networks. Besides the traditional parameters, including centers, widths and output weights, the input weights on the connections between input layer and hidden layer are also adjusted during the tra...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 23; H. 4; S. 609 - 619
Hauptverfasser:	Tiantian Xie, Hao Yu, Hewlett, J., Rozycki, P., Wilamowski, B.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.04.2012 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm design and analysis Algorithms Applied sciences Artificial intelligence Computation Computer science; control theory; systems Connectionism. Neural networks Exact sciences and technology ISO Jacobian matrices Levenberg-Marquardt algorithm Mathematical analysis Multiplication Networks Neural networks Radial basis function Radial basis function networks second order algorithm Software algorithms Studies Training Vectors Vectors (mathematics) Input output Matrix product Neural network Jacobi matrix Radial basis function Levenberg Marquardt algorithm Classification Matrix calculus radial basis function networks second order algorithm ISO standard Hessian matrices Levenberg-Marquardt algorithm
ISSN:	2162-237X, 2162-2388
Online-Zugang:	Volltext
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