Solution of inverse heat conduction problems using Kalman filter-enhanced Bayesian back propagation neural network data fusion

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.