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Multi-innovation identification algorithm of neural network based on generalized objective function
XU Bao-chang, LIU Xin-le
(College of Geophysics and Information Engineering in China University of Petroleum, Beijing 102249, China)
Abstract:
To improve the identification accuracy and robustness to noise of dynamic neural network learning algorithm,multi-innovation identification algorithm based on a generalized objective function was presented. The generalized function based on multi-innovation theory was constructed by combining an auxiliary constraint term with the least mean square error. The weight matrix of output layer was trained using the generalized function. The recursive equations for training weight matrix of output layer were derived using Newton iterative algorithm. Compared with the existed second-order learning algorithm,this algorithm has stronger robustness,better convergent rate and accuracy. Simulation results show the efficiency of the new algorithm.
Key words:  system identification  generalized objective function  neural network  multi-innovation