Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1179
Title: Step acceleration based training algorithm for feedforward neural networks
Authors: Li, Y
Wang, K
Zhang, DD 
Keywords: Computation theory
Computer simulation
Convergence of numerical methods
Extrapolation
Feedforward neural networks
Functions
Issue Date: 2002
Publisher: IEEE Computer Society
Source: 16th International Conference on Pattern Recognition : August 11-15, 2002, Québec City, Québec, Canada : proceedings, v. 2, p. 84-87 How to cite?
Abstract: This paper presents a very fast step acceleration based training algorithm (SATA) for multilayer feedforward neural network training. The most outstanding virtue of this algorithm is that it does not need to calculate the gradient of the target function. In each iteration step, the computation only concentrates on the corresponding varied part. The proposed algorithm has attributes in simplicity, flexibility and feasibility, as well as high speed of convergence. Compared with the other methods, including the conventional BP, the conjugate gradient (CG), and the BP based on weight extrapolation (BPWE), many simulations have confirmed the superiority of this algorithm in terms of converging speed and computation time required.
URI: http://hdl.handle.net/10397/1179
ISBN: 0-7695-1695-X
0-7695-1696-3
ISSN: 1051-4651
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