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Memristive Radial Basis Function Neural Network for Parameters Adjustment of PID Controller

  • Xiaojuan Li
  • Shukai Duan
  • Lidan Wang
  • Tingwen Huang
  • Yiran Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8866)

Abstract

Radial basis function (RBF) based-identification proportional–integral–derivative (PID) can automatically adjust the parameters of PID controller with strong self-organization, self-learning and self-adaptive ability. However, the compound controller has complex weight updating algorithm and large calculation. Memristor, applied well to the investigation of storage circuit and artificial intelligence, is a nonlinear element with memory function. Thus, it can be introduced to RBF neural network as electronic synapse to save and update the synaptic weights. This paper builds a model of memristive RBF-PID (MRBF-PID), and proposes the updating algorithm of weight upon memristance. The proposed MRBF-PID is used for the control of a nonlinear system. Its controlling effect is showed by numerical simulation experiment.

Keywords

Memristor Radial basis function neural network PID controller Simulink model 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiaojuan Li
    • 1
  • Shukai Duan
    • 1
  • Lidan Wang
    • 1
  • Tingwen Huang
    • 2
  • Yiran Chen
    • 3
  1. 1.College of Electronic and Information EngineeringSouthwest UniversityChongqingChina
  2. 2.Department of Electrical and Computer EngineeringTexas A&M UniversityDohaQatar
  3. 3.Department of Electrical and Computer EngineeringUniversity of PittsburghPittsburghUSA

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