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一種動態自適應最近鄰聚類學習算法在工業污水處理中的應用

Application of the dynamic self-adaptive nearest neighbor clustering algorithm in an industrial waste water treatment system

  • 摘要: 為了建立工業污水pH值中和系統的正模型,研究了具有大滯后非線性特性的加藥中和過程。利用一種動態自適應最近鄰聚類(DANNC)學習算法,全面調整網絡參數完成了污水pH值加藥中和控制系統網絡的學習和訓練。采用中和過程神經網絡內模控制系統的逆模型充當控制器,進行了各種工業條件下污水中和的仿真實驗。結果表明,該系統實現了△pH≤0.2的工業污水的控制精度目標,系統實時跟蹤和抗干擾性良好。

     

    Abstract: In order to establish a positive model of pH value control, the process with severe non-linearity and serious lag of neutralization action was studied by adding medicine in an industrial waster water neutralization control system. A novel kind of Dynamic Adaptive Nearest Neighbor Clustering (DANNC) algorithm was adopted, and a strategy by adjusting the parameter in the entire neural network to finish the task of learning and training of the neural network (NN) was applied. The NN internal model control system for pH value of neutralization, which serves as a controller of the converse model was designed, and different kinds of simulation experiments were carried. The results showed that the accuracy of the pH control system is △pH≤0.2, which satisfied the requirement of the real time adding medicine track and anti-jamming abilities in industrial application.

     

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