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Title: Step acceleration based training algorithm for feedforward neural networks
Authors: Li, Y
Wang, K
Zhang, DD 
Issue Date: 2002
Source: 16th International Conference on Pattern Recognition : August 11-15, 2002, Québec City, Québec, Canada : proceedings, v. 2, p. 84-87
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.
Keywords: Computation theory
Computer simulation
Convergence of numerical methods
Feedforward neural networks
Publisher: IEEE Computer Society
ISBN: 0-7695-1695-X
ISSN: 1051-4651
Rights: © 2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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