Abstract: In view of insufficient accuracy in the existing application of nonlinear filtering algorithm for neural network training,a novel neural network training algorithm based on iterated cubature Kalman filter was proposed.Firstly,the connection weights and bias of feedforward neural network are used as the state vector to establish the state space mo-del.Secondly, using the Spherical-Radial standard to generate cubature points,the state estimation and covariance acquired during the measurement update process are optimized based on Gauss-Newton iteration strategy.The training effect of neural network connecting weights and bias is enhanced through the improvement of the estimation precision of cubature Kalman filter.The theoretical analysis and simulation results show the feasibility and effectiveness of the algorithm.

Key words: Feedforward neural network,State-space model,Cubature kalman filter,Gauss-Newton iterate