Application of RBF Neural Networks Based on a New Hybrid Optimization Algorithm in Flotation Process

  • Yong Zhang
  • Jie-Sheng Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


An inferential estimation strategy of quality indexes of flotation process based on principal component analysis (PCA) and radial basis function neural network (RBFNN) is proposed. Firstly, the process prior knowledge and PCA method are used to simplify the networks’ input dimension and to choose the secondary variables. Then a new hybrid optimization algorithm of RBFNN is developed. The algorithm includes simplified rival penalized competitive learning method (SRPCL) to make an adaptive clustering of networks’ input pattern and recursive least squares method (LSM) with forgetting factor to update networks’ weights. The simulation results show that this inference estimation strategy has high predictive accuracy in flotation process.


Input Pattern Radial Basis Function Neural Network Flotation Process Connective Weight Radial Basis Function Neural Network Model 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yong Zhang
    • 1
  • Jie-Sheng Wang
    • 1
  1. 1.Anshan University of Science & TechnologyAnshanP.R. China

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