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Conference Paper: Sequential RBF function estimator: memory regression network
| Title | Sequential RBF function estimator: memory regression network |
|---|---|
| Authors | |
| Issue Date | 2004 |
| Publisher | Institute of Electrical and Electronics Engineers. The Journal's website is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9622 |
| Citation | The 2004 IEEE International Conference on Systems, Man and Cybernetics (SMC 2004), Hague, The Netherlands, 10-13 October 2004. In IEEE International Conference on Systems, Man, and Cybernetics. Conference Proceedings, 2004, v. 5, p. 4815-4820 How to Cite? |
| Abstract | The newal-network training algorithm can be divided into 2 categories: (I) Batch mode and (2) Sequential mode. In this paper, a novel online RBF network called "Memory Regression Network (MRN)" is proposed. Different from the previous approaches [2, 11], MRN involves two types of memories: Experience and Neuron, which handle short and long term memories respectively. By simulating human's learning behavior, a given function can be estimated without memorizing the whole training set. Two sets of function estimation experiments are examined in order to illustrate the performance of the proposed algorithm. The results show that MRN can effectively approximate the given function within a reasonable time and acceptable mean square error. © 2004 IEEE. |
| Persistent Identifier | http://hdl.handle.net/10722/196655 |
| ISBN | |
| ISSN | 2020 SCImago Journal Rankings: 0.168 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Chow, C-K | - |
| dc.contributor.author | Tsui, H-T | - |
| dc.date.accessioned | 2014-04-24T02:10:31Z | - |
| dc.date.available | 2014-04-24T02:10:31Z | - |
| dc.date.issued | 2004 | - |
| dc.identifier.citation | The 2004 IEEE International Conference on Systems, Man and Cybernetics (SMC 2004), Hague, The Netherlands, 10-13 October 2004. In IEEE International Conference on Systems, Man, and Cybernetics. Conference Proceedings, 2004, v. 5, p. 4815-4820 | - |
| dc.identifier.isbn | 0-7803-8566-7 | - |
| dc.identifier.issn | 1062-922X | - |
| dc.identifier.uri | http://hdl.handle.net/10722/196655 | - |
| dc.description.abstract | The newal-network training algorithm can be divided into 2 categories: (I) Batch mode and (2) Sequential mode. In this paper, a novel online RBF network called "Memory Regression Network (MRN)" is proposed. Different from the previous approaches [2, 11], MRN involves two types of memories: Experience and Neuron, which handle short and long term memories respectively. By simulating human's learning behavior, a given function can be estimated without memorizing the whole training set. Two sets of function estimation experiments are examined in order to illustrate the performance of the proposed algorithm. The results show that MRN can effectively approximate the given function within a reasonable time and acceptable mean square error. © 2004 IEEE. | - |
| dc.language | eng | - |
| dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's website is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9622 | - |
| dc.relation.ispartof | IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings | - |
| dc.rights | ©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | - |
| dc.title | Sequential RBF function estimator: memory regression network | - |
| dc.type | Conference_Paper | - |
| dc.description.nature | published_or_final_version | - |
| dc.identifier.doi | 10.1109/ICSMC.2004.1401293 | - |
| dc.identifier.scopus | eid_2-s2.0-15744385926 | - |
| dc.identifier.volume | 5 | - |
| dc.identifier.spage | 4815 | - |
| dc.identifier.epage | 4820 | - |
| dc.publisher.place | United States | - |
| dc.customcontrol.immutable | sml 160603 amended | - |
| dc.identifier.issnl | 1062-922X | - |