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Self-organization hybrid evolution learning algorithm for recurrent wavelet-based neuro-fuzzy identifier design.
- Source :
-
Journal of Intelligent & Fuzzy Systems . 2013, Vol. 24 Issue 3, p521-533. 13p. 2 Diagrams, 3 Charts, 5 Graphs. - Publication Year :
- 2013
-
Abstract
- In this paper, a recurrent wavelet-based neuro-fuzzy identifier (RWNFI) with a self-organization hybrid evolution learning algorithm (SOHELA) is proposed for solving various identification problems. In the proposed SOHELA, the group-based symbiotic evolution (GSE) is adopted such that each group in the GSE represents a collection of only one fuzzy rule. The proposed SOHELA consists of structure learning and parameter learning. In structure learning, the proposed SOHELA uses the self-organization algorithm (SOA) to determine a suitable rule number in the RWNFI. In parameter learning, the proposed SOHELA uses the data mining-based selection method (DMSM) and the data mining-based crossover method (DMCM) to determine groups and parent groups using the data mining method called the frequent pattern growth (FP-Growth) method. Based on identification simulations, the excellent performance of the proposed SOHELA compares with other various existing models. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10641246
- Volume :
- 24
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Journal of Intelligent & Fuzzy Systems
- Publication Type :
- Academic Journal
- Accession number :
- 85932525
- Full Text :
- https://doi.org/10.3233/ifs-2012-0540