Research On The Application Of General Radial Basis Function Neural Network In Analysis Of Dam Safety Monitoring Data

There are many dams and embankments built or building in our country, the height and size of these dams and embankment is increasing. So it's very important of safety of these.Because of Neural Network's powerful ability of studying and modifying errors by itself and its characteristic of approaching nonlinear system in theory, it has been applied to many fields.This paper describes regression analysis method , statistic model of dam distortion monitoring data and basic conception of Neural Network., and introduces Radial Basis Function Neural Network model .It's mapping from input layer to output layer is non-linear , but according as adjustable parameter the network's output layer is linear .Once the hub of this network is fixed on this mapping relationship is confirmed . In this paper Recursion Orthogonal Least Squares method is applied to fix on training hub rapidly and predigesting framework of the network. General Regression Neural Network is one transfiguration of RBFNN, witch applies theory of regularization and dimension of Green-function witch is less than input layer to predigesting the framework and compute rapidly. In this paper GRNN is applied to construct model of the large dam safety monitoring . Comparing to BP model ,GRNN model avoids appearance of iterative calculating process and local extremum of BP model, and has better combine and prediction precision . Analysis in this paper proves that , GRNN model can be practiced in data processing of the large dam safety monitoring and can get optimized calculation result.