基于对角递归神经网络的建模及应用

Modeling and Application Based on Diagonal Recurrent Neural Network

  • 摘要: 介绍了对角递归神经网络,针对BP算法收敛慢的缺点,将递推预报误差学习算法应用到神经网络权值和域值的训练.通过对非线性系统辨识的仿真及在磷化温控系统建模中的应用,验证了这种建模方法的有效性.

     

    Abstract: A simple recurrent neural network named as diagonal recurrent neural network was studied. To overcome the slow convergence of BP algorithm, the recursive prediction error (RPE) algorithm was proposed, which can train both the weight and the bias. A given model was identified by using diagonal recurrent neural network trained with RPE algorithm, and the model of a phosphating temperature control system was established. Both simulation and experiment demonstrate the effectiveness of the proposed algorithm.

     

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