Journal
APPLIED MATHEMATICS AND COMPUTATION
Volume 195, Issue 1, Pages 66-75Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.amc.2007.04.088
Keywords
dynamic; all parameters; adaptive; genetic algorithms; BP neural network; structure identification
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In this paper, a dynamic all parameters adaptive BP neural networks model is proposed by fusing genetic algorithms (GAs), simulated annealing (SA) and error back propagation neural network (BPNN) to offset the demerits of one paradigm by the merits of another. Adopting multi-encoding, the model can optimize the input nodes, hidden nodes, transfer function, weights and bias of BP networks dynamically and adaptively. Under accurate premise, the simple architecture (less input and hidden nodes) of network model is constructed in order to improve networks' adaptation and generalization ability, and to greatly reduce the subjective choice of structural parameters. The results of application on oil reservoir prediction show that the proposed model with comparatively simple structure can meet the precision request and enhance the generalization ability. (c) 2007 Elsevier Inc. All rights reserved.
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