A dynamic all parameters adaptive BP neural networks model and its application on oil reservoir prediction

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...

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Bibliographic Details
Published in:Applied mathematics and computation 2008-01, Vol.195 (1), p.66-75
Main Authors: Yu, Shiwei, Zhu, Kejun, Diao, Fengqin
Format: Article
Language:English
Subjects:
Adaptive
All parameters
BP neural network
Dynamic
Exact sciences and technology
Genetic algorithms
Mathematical analysis
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Sciences and techniques of general use
Structure identification
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Summary: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.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2007.04.088