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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tienan Liu Xuezhong Guan Zhiyong Liu Aihua Xie Hang Zhang |
| Copyright Year | 2002 |
| Description | Author affiliation: Dept. of Autom. & Control Eng., Daqing Pet. Inst., Heilongjiang, China (Tienan Liu; Xuezhong Guan) |
| Abstract | In this paper, the RBF neural network (RBFNN) is used as a modeling framework to solve the identification problem of nonlinear systems. First, it is proposed a kind of optimal selection cluster algorithm. By this algorithm, it is optimally gained the hidden layer node number of RBFNN in terms of input samples. At the same time, the initial parameter values of RBF are obtained. Then, the parameters of RBF are estimated by the gradient algorithm with momentum terms, and the weights of RBFNN are identified by the recursive least square algorithm. The above two algorithms are alternately iterated. By the above hybrid algorithms, it is not only raised identification precision of RBFNN, but also improved the generalization property of the net. The validity of the scheme described is proved. |
| Sponsorship | Tongji Univ. Shanghai Jiaotong Univ. East China Univ. Sci. & Technol. IEEE Robotics & Autom. Soc. IEEE Control Syst. Soc. Beijing Chapter Chinese Assoc. Autom. Shanghai Assoc. Automation |
| Starting Page | 1408 |
| Ending Page | 1411 |
| File Size | 224300 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780372689 |
| DOI | 10.1109/WCICA.2002.1020813 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2002-06-10 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Clustering algorithms Petroleum Control engineering Machinery Nonlinear systems Parameter estimation Recursive estimation Least squares approximation Least squares methods |
| Content Type | Text |
| Resource Type | Article |
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