期刊
INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS
卷 27, 期 8, 页码 651-667出版社
WILEY
DOI: 10.1002/nag.291
关键词
back-propagation neural network; Bayesian neural network; genetic algorithms; neural network; non-linear modelling; piling; skin friction
is growing interest in the use of back-propagation neural networks to model non-linear multivariate problems in geotechnical engineering. To overcome the shortcomings of the conventional back-propagation neural network, such as overfitting, where the neural network learns the spurious details and noise in the training examples, a hybrid back-propagation algorithm has been developed. The method utilizes the genetic algorithms search technique and the Bayesian neural network methodology. The genetic algorithms enhance the stochastic search to locate the global minima for the neural network model. The Bayesian inference procedures essentially provide better generalization and a statistical approach to deal with data uncertainty in comparison with the conventional back-propagation. The uncertainty of data can be indicated using error bars. Two examples are presented to demonstrate the convergence and generalization capabilities of this hybrid algorithm. Copyright (C) 2003 John Wiley Sons, Ltd.
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