Solution of inverse heat conduction problems using Kalman filter-enhanced Bayesian back propagation neural network data fusion
Solution of inverse heat conduction problems using Kalman filter-enhanced Bayesian back propagation neural network data fusion
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DOI：
10.1016/j.ijheatmasstransfer.2006.11.019
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发表时间：
2007-06
影响因子：
5.2
通讯作者：
S. Deng;Y. Hwang
中科院分区：
文献类型：
--
作者：
S. Deng;Y. Hwang
本文提出了一种利用卡尔曼滤波增强贝叶斯反向传播神经网络（KF-B2PNN）分析热传导逆问题的有效方法。使用连续时间模拟Hopfield神经网络制备KF-B2PNN所需的训练数据，然后通过一系列数值模拟来检验KF-B2PNN方案的性能。结果表明，所提方法能在可接受的误差范围内预测当前逆问题中的未知参数。KF-B2PNN方案的性能优于使用Levenberg-Marquardt算法训练的独立反向传播神经网络。
This paper presents an efficient technique for analyzing inverse heat conduction problems using a Kalman Filter-enhanced Bayesian Back Propagation Neural Network (KF-B2PNN). The training data required for the KF-B2PNN are prepared using the Continuous-time analogue Hopfield Neural Network and the performance of the KF-B2PNN scheme is then examined in a series of numerical simulations. The results show that the proposed method can predict the unknown parameters in the current inverse problems with an acceptable error. The performance of the KF-B2PNN scheme is shown to be better than that of a stand-alone Back Propagation Neural Network trained using the Levenberg–Marquardt algorithm.