A Kind Improved Model of Information Fusion

Information fusion is an important research field, one major theory and technology is neural networks especially back-propagation (BP) neural network. Meanwhile BP neural network has been applied in many fields. But traditional BP neural network has some faults, such as bad convergence rate and low learning rate aiming at huge date sets, poor generalization, poor ability of error weight update and batch learning. Aiming at these faults, some improved methods are proposed to solve these problems, one of methods is BP neural network based on Kalman Filter which can solve before-mentioned faults partly. But present methods of BP neural network based on Kalman Filter can not do batch processing and study multi-sample conditions. Improved BP neural network based on Kalman Filter is proposed depending on present BP neural network based on Kalman Filter. The idea of new method includes two steps, firstly we obtain the update of estimation weight, secondly we use the obtained results to mend the Kalman Gain for new update of time and measurement, at the some time the new algorithm can adopt batch processing to learning neural network. Experiments show the new algorithm can solve high-dimensional, large computation problem, keeping robustness and improving the learning efficiency.