SAM-FNN training based on particle filter

The most characteristic of Lunar Rover Motion Planning and Control is unstructured of lunar terrain, and do not establish accurate mathematical model. In order to used the method of environmental model and analysis to study the lunar rover motion planning. Combining the nature of convex combination on this paper, proposes the fuzzy neural network system based on SAM which apply particle filter training algorithm. Proved the SAM-FNN is continuity, stability and accessibility. The Lunar Rover´s translational speed and rotation speed are smooth and continuous changes. Particle filter training algorithm to overcome the weakness that current training algorithms of Neural Network is likely to trap in local minimum. It is an efficient dealing with nonlinear/non-Gaussian problems. Simulation results show that its performance is markedly superior to those available.