Advertisement

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)

Abstract

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.

Keywords

Input Pattern Radial Basis Function Neural Network Flotation Process Connective Weight Radial Basis Function Neural Network Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhang, Y., He, H.J., Wang, W.: Research on Technology for Iron Increasing and Silicon Reduction. Mining and Metallurgical Engineering 23(1), 34–37 (2003)MathSciNetGoogle Scholar
  2. 2.
    Ipek, H., Ankara, H.: The Application of Statistical Process Control. Minerals Engineering 12(7), 827–835 (1999)CrossRefGoogle Scholar
  3. 3.
    Wang, X.D., Shao, H.H.: The Theory of RBF Neural Network and Its Application in Control. Information and Control 26(4), 272–284 (1997)Google Scholar
  4. 4.
    Wang, X.F., Wan, Z.Q., Song, W.Z.: A New Hybrid Recursive Learning Algorithm for Ra-dial Basis Function Neural Networks. Control Theory and Application 15(2), 272–276 (1998)Google Scholar

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

Personalised recommendations