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基于對角遞歸神經網絡的建模及應用

Modeling and Application Based on Diagonal Recurrent Neural Network

  • 摘要: 介紹了對角遞歸神經網絡,針對BP算法收斂慢的缺點,將遞推預報誤差學習算法應用到神經網絡權值和域值的訓練.通過對非線性系統辨識的仿真及在磷化溫控系統建模中的應用,驗證了這種建模方法的有效性.

     

    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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