Evolutionary Computation for Dynamic Parameter Optimisation of Evolving Connectionist Systems for On-line Prediction of Time Series with Changing Dynamics
| dc.contributor.author | Kasabov, N | |
| dc.contributor.author | Song, Q. | |
| dc.contributor.author | Nishikawa, I. | |
| dc.date.accessioned | 2009-05-27T22:18:48Z | |
| dc.date.available | 2009-05-27T22:18:48Z | |
| dc.date.copyright | 2003 | |
| dc.date.created | 2003 | |
| dc.date.issued | 2003 | |
| dc.description.abstract | The paper describes a method of using evolutionary computation technique for parameter optimisation of evolving connectionist systems (ECOS) that operate in an online, life-long learning mode. ECOS evolve their structure and functionality from an incoming stream of data in either a supervised-, or/and in an unsupervised mode. The algorithm is illustrated on a case study of predicting a chaotic time-series that changes its dynamics over time. With the on-line parameter optimisation of ECOS, a faster adaptation and a better prediction is achieved. The method is practically applicable for real time applications. | |
| dc.identifier.uri | https://hdl.handle.net/10292/593 | |
| dc.publisher | IEEE | |
| dc.relation.uri | http://ieeexplore.ieee.org/iel5/8672/27472/01223386.pdf | |
| dc.rights | ©2003 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.rights.accessrights | OpenAccess | |
| dc.source | International Joint Conference on Neural Networks, 1, 438-443 | |
| dc.title | Evolutionary Computation for Dynamic Parameter Optimisation of Evolving Connectionist Systems for On-line Prediction of Time Series with Changing Dynamics | |
| dc.type | Conference Proceedings |