Name
Title | ||
|---|---|---|
Application of RBF neural networks based on a new hybrid optimization algorithm in flotation process |
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11760191_157 | ISNN (2) |
Keywords | Field | DocType |
rbf neural network,input pattern,input dimension,squares method,flotation process,inferential estimation strategy,pca method,new hybrid optimization algorithm,process prior knowledge,inference estimation strategy,adaptive clustering,principal component analysis | Least squares,Competitive learning,Radial basis function,Pattern recognition,Inference,Computer science,Artificial intelligence,Artificial neural network,Cluster analysis,Machine learning,Recursive least squares filter,Principal component analysis | Conference |
Volume | ISSN | ISBN |
3973 | 0302-9743 | 3-540-34482-9 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Zhang | 1 | 354 | 21.23 |
| Jie-sheng Wang | 2 | 3 | 3.44 |