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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 23; číslo 4; s. 609 - 619
Hlavní autoři:	Tiantian Xie, Hao Yu, Hewlett, J., Rozycki, P., Wilamowski, B.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.04.2012 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!