The centroid and attitude of target must be predicted in target tracking of IR image for increasing capture probability. CMAC estimator can effectually resolve conflict between operational counts and predicting preci...The centroid and attitude of target must be predicted in target tracking of IR image for increasing capture probability. CMAC estimator can effectually resolve conflict between operational counts and predicting precision. CMAC estimator is trained with a linear model, then the centroid and attitude are predicted. It is trained once by actual error in each frame to reduce the estimate error. CMAC has excellent predicting precision and small operational counts, it adapts to real time processing for target tracking. The experimental results show that CMAC can accurately estimate the centroid and attitude of target. It adapts to change of model and has robustness.展开更多
A novel sequential neural network learning algorithm for function approximation is presented. The multi-step-ahead output predictor of the stochastic time series is introduced to the growing and pruning network for co...A novel sequential neural network learning algorithm for function approximation is presented. The multi-step-ahead output predictor of the stochastic time series is introduced to the growing and pruning network for constructing network structure. And the network parameters are adjusted by the proportional differential filter （PDF） rather than EKF when the network growing criteria are not met. Experimental results show that the proposed algorithm can obtain a more compact network along with a smaller error in mean square sense than other typical sequential learning algorithms.展开更多
文摘The centroid and attitude of target must be predicted in target tracking of IR image for increasing capture probability. CMAC estimator can effectually resolve conflict between operational counts and predicting precision. CMAC estimator is trained with a linear model, then the centroid and attitude are predicted. It is trained once by actual error in each frame to reduce the estimate error. CMAC has excellent predicting precision and small operational counts, it adapts to real time processing for target tracking. The experimental results show that CMAC can accurately estimate the centroid and attitude of target. It adapts to change of model and has robustness.
基金Sponsored by the Ministerial Level Foundation(230032)
文摘A novel sequential neural network learning algorithm for function approximation is presented. The multi-step-ahead output predictor of the stochastic time series is introduced to the growing and pruning network for constructing network structure. And the network parameters are adjusted by the proportional differential filter （PDF） rather than EKF when the network growing criteria are not met. Experimental results show that the proposed algorithm can obtain a more compact network along with a smaller error in mean square sense than other typical sequential learning algorithms.
