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Tok, DKS, Yu, DL, Mathews, C, Zhao, D-Y and Zhu, Q-M (2014) Adaptive structure radial basis function network model for processes with operating region migration. Neurocomputing, 155. pp. 186-193. ISSN 0925-2312
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NEUCOM-S-14-01087.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (465kB) | Preview |
Abstract
An adaptive structure radial basis function (RBF) network model is proposed in this paper to model nonlinear processes with operating region migration. The recursive orthogonal least squares algorithm is adopted to select new centers on-line, as well as to train the network weights. Based on the R matrix in the orthogonal decomposition, an initial center bank is formed and updated in each sample period. A new learning strategy is proposed to gain information from the new data for network structure adaptation. A center grouping algorithm is also developed to divide the centers into active and non-active groups, so that a structure with a smaller size is maintained in the final network model. The proposed RBF model is evaluated and compared to the two fixed-structure RBF networks by modeling a nonlinear time-varying numerical example. The results demonstrate that the proposed adaptive structure model is capable of adapting its structure to fit the operating region of the process effectively with a more compact structure and it significantly outperforms the two fixed structure RBF models.
| Item Type: | Article |
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| Uncontrolled Keywords: | 08 Information And Computing Sciences, 09 Engineering, 17 Psychology And Cognitive Sciences |
| Subjects: | B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Divisions: | Electronics & Electrical Engineering (merged with Engineering 10 Aug 20) Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20) |
| Publisher: | Elsevier |
| Related URLs: | |
| Date Deposited: | 06 Nov 2018 10:35 |
| Last Modified: | 04 Sep 2021 04:30 |
| DOI or ID number: | 10.1016/j.neucom.2014.12.030 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/2046 |
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