Fuzzy least squares support vector machine soft measurement model based on adaptive mutative scale chaos immune algorithm

Autor:	Tao-sheng Wang, Hong-yan Zuo
Rok vydání:	2014
Předmět:	CHAOS (operating system) Support vector machine Character (mathematics) Scale (ratio) Robustness (computer science) Control theory Least squares support vector machine Metals and Alloys General Engineering Complex system Algorithm Fuzzy logic Mathematics
Zdroj:	Journal of Central South University. 21:593-599
ISSN:	2227-5223 2095-2899
DOI:	10.1007/s11771-014-1978-4
Popis:	In order to enhance measuring precision of the real complex electromechanical system, complex industrial system and complex ecological & management system with characteristics of multi-variable, non-liner, strong coupling and large time-delay, in terms of the fuzzy character of this real complex system, a fuzzy least squares support vector machine (FLS-SVM) soft measurement model was established and its parameters were optimized by using adaptive mutative scale chaos immune algorithm. The simulation results reveal that fuzzy least squares support vector machines soft measurement model is of better approximation accuracy and robustness. And application results show that the relative errors of the soft measurement model are less than 3.34%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4bd41c3c8f8e5da0658facd37e7f437e https://doi.org/10.1007/s11771-014-1978-4 Zobrazit plný text záznamu Full text from SpringerLink