Research On Data Fusion Algorithms Based On Neural Network And Filter Theory

In recent years, multi-sensor information fusion theory and its applications have obtained rapid advances, which become to be an important research field. Among these methods, Neural Networks, Fuzzy Reasoning, Kalman Filter, Particle Filter and other data fusion methods have been a hot spot of research all along. In view of different actual application requiremrnts, how to combine these methods with each other is a new trend in present.In this paper, several kinds of data fusion methods are studied and they are applied to spacecraft reliability estimation. In the theoretical respect such as Neural Networks, Kalman Filter,Particle Filter and their combination methods, the following research work have been completed in this paper.Firstly, a new BP learning algorithm based on DFP method and the trust region method is presented in this paper in order to get the solutions for some learning problems of traditional BP Neural Network, such as the slow rate of convergence and poor stabilization. DFP method is a classical Quasi-Newton algorithm which is super-linear convergent in the optimization theory. This algorithm maintains the highly efficient searching and super-linear convergence. In the mean time, it needs less computation cost and is proper to deal with large residual problems. Furthermore, combining with trust region method, this algorithm possesses the global convergence property and stability which make it more applicable. The simulated experiments show that the new algorithm has the advantages of fast learning speed, little computation cost, high accuracy and properness for large residual problems.Secondly, aiming at the problem that too much time of the large scale samples clustering and the result of the clustering excessively relying on the experiential parameters, an efficient self-expanded clustering algorithm with parameter-adjustable based on density units(PASCDU)is proposed in this paper. The whole data space is divided into several equal density units, before each data point is mapped into the relevant density unit according to the data point charter. Then cluster extends around from large density unit, until average density under the low-limit or cluster extends to the cluster's edge. During the clustering process, we can prevent clusters from excessively extending by setting low-limit and average-limit. If the ratio of effectual samples is under the designate value, then automatically adjusts this value, and then cluster again.Thirdly, although particle filter implements recursive Bayesian filter based on Monte Carlo simulation, and it shows the merits in dealing with the nonlinear and non-Gaussian models, particle degeneration is difficult to conquer. To combat particle degeneration, a new importance resampling algorithm, i.e. divisional resampling is proposed. The mainly idea of divisional resampling is to combine polynomial resampling method and layered one, divide stochastic set of data into several sets which can insure that each of the sets has stochastic array of data and all the sets have an array according to sort ascending. Compared to the other general resampling algorithms, the method proposed improves the average performance of particle filter.Finally, aiming at the problem of the learning algorithm of neural network based on Kalman Filter being good at dealing with samples with great noise, being not good at dealing with batch manipulation and with low learning accuracy, this paper develops improved BP NN learning algorithm based on Kalman Filter. The main idea of improved BP learning algorithm based on Kalman Filter lies in: First, updates the estimation state parameters by other learning algorithm in the time updating process, and then modify the formula of Kalman Gain by these results. In this way, some new time update formulas and measure update formulas come up. This new method solves the problems of dimension disaster and large computation. And it enhances the robust of Neural Network and improves the learning ability by batch learning.The above mehods are proved to be efficient and work well by the simulation experimental results.