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摘要：In this paper we apply Bayesian neural networks to life modeling and prediction with real data from a dynamically tuned gyroscopes (DTG). The Bayesian approach provides consistent way to inference by integrating the evidence from data with prior knowledge from the problem. Bayesian neural networks can overcome the main difficulty in controlling the model's complexity of modeling building of standard neural network. And the Bayesian approach offers efficient tools to avoid overfitting even with very complex models, and facilitates estimation of the confidence intervals of the results. In this paper, we review the Bayesian methods for neural networks and present results of case study in life modeling and prediction of DTG.

卷号：3174

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是否译文：否