The Explores Of Optimum Nonlinear Filtering Solved By Neural Networks

This paper explores the neural networks approach to optimum nonlinear filtering, the main research work is as follows:It analysis the actualities and the developing trend about the neural networks approach to optimum nonlinear filtering, and it surveys neural networks and the classical theory about the optimum nonlinear filtering. Among them include not only the nonlinear Least-Square (LMS) estimation (spread of Kalman filter) but also the nonlinear Least Mean Square estimation (LS). At the same time it analysis feasibility of integration of neural networks and optimum nonlinear filtering.It discusses the Back Propagation (BP) network and the Radial Basis Function (RBF) network to optimum nonlinear filtering, and it makes use of MATLAB as simulation software, and applying the BP network and the Radial Basis Function (RBF) network simulate realism examples, it obtain a series of simulation waves, thus , the accuracy of the simulation model is validated from the manner of experiments.Finally, it by the computer simulation verifies the superiority for the RBF's in nonlinear filtering, the RBF have many advantage such as shorter training time, least note and higher accuracy. It can be seen, the ANN used in the optimum nonlinear filtering will be the tendency of the filtering fields.