Hybrid Evolutionary Soft-Computing Approach for Unknown System Identification
| Autor: | Chunshien Li, Jiann-Der Lee, Zen-Shan Chang, Kuo-Hsiang Cheng |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Soft computing
System identification Hybrid algorithm Evolutionary computation Identification (information) symbols.namesake Artificial Intelligence Hardware and Architecture Gaussian function symbols Random optimization Computer Vision and Pattern Recognition Electrical and Electronic Engineering Algorithm Software Membership function Mathematics |
| Zdroj: | IEICE Transactions on Information and Systems. :1440-1449 |
| ISSN: | 1745-1361 0916-8532 |
| DOI: | 10.1093/ietisy/e89-d.4.1440 |
| Popis: | A hybrid evolutionary neuro-fuzzy system (HENFS) is proposed in this paper, where the weighted Gaussian function (WGF) is used as the membership function for improved premise construction. With the WGF, different types of the membership functions (MFs) can be accommodated in the rule base of HENFS. A new hybrid algorithm of random optimization (RO) algorithm incorporated with the least square estimation (LSE) is presented. Based on the hybridization of RO-LSE, the proposed soft-computing approach overcomes the disadvantages of other widely used algorithms. The proposed HENFS is applied to chaos time series identification and industrial process modeling to verify its feasibility. Through the illustrations and comparisons the impressive performances for unknown system identification can be observed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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