Study on Nonlinear Optimal Estimation for Neural Networks Data Fusion
Yao Dai · Dianzi xuebao · 2005
A new model and algorithm to realize adaptive adjustment of the weights of NN and to make global fusion information optimal were presented.The method utilizes Unscented Kalman filter(UKF) for nonlinear optimal estimation to solve the problem that weights of neural networks are not be on-line trained in data fusion.Applies the above project to a multi-sensors vessel integrated navigation system,obtains actual data from the integrated navigation system of DGPS/GPS/RLC/compass.First,using UKF methods estimates and filters the location information,then,NNKF and NNUKF are used to fuse them.The results of experiment and simulation show that the proposed approach is very useful for improving the accuracy and calculation speed of the system.