2011
DOI: 10.1016/j.asoc.2009.12.041
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Identification of the linear parts of nonlinear systems for fuzzy modeling

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Cited by 9 publications
(3 citation statements)
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“…Fuzzy rules can be generated through training algorithms such as the descent gradient method (Zhao et al, 2008;Aflab and Kadri, 2013), the recursive least squares algorithm (Xu and Xuesong, 2014) and the orthogonal least squares method (Soltani et al, 2011). Hybrid algorithms are also interesting training methods in order to obtain simple and accurate models (Chen and Linkens, 2004;Abdelazim and Malik, 2005;Jin, 2000;Lavygina and Hodashinsky, 2011;Chuang et al, 2001Chuang et al, , 2009Rezaei Sadrabadi and Fazel Zarandi, 2011). For example, the clustering approach and the descent gradient method were used for the estimation and the reduction of fuzzy models (Chen and Linkens, 2004).…”
Section: Introductionmentioning
confidence: 99%
“…Fuzzy rules can be generated through training algorithms such as the descent gradient method (Zhao et al, 2008;Aflab and Kadri, 2013), the recursive least squares algorithm (Xu and Xuesong, 2014) and the orthogonal least squares method (Soltani et al, 2011). Hybrid algorithms are also interesting training methods in order to obtain simple and accurate models (Chen and Linkens, 2004;Abdelazim and Malik, 2005;Jin, 2000;Lavygina and Hodashinsky, 2011;Chuang et al, 2001Chuang et al, , 2009Rezaei Sadrabadi and Fazel Zarandi, 2011). For example, the clustering approach and the descent gradient method were used for the estimation and the reduction of fuzzy models (Chen and Linkens, 2004).…”
Section: Introductionmentioning
confidence: 99%
“…Sadrabadi and Zarandi [21] propose an algorithm to classify input-output points into two categories: the points located in the linear parts and the point located in the extrema.…”
Section: Clusteringmentioning
confidence: 99%
“…Подавляющее большинство методов формирования структуры основано на методах нечеткого кластерного анализа, среди которых наиболее часто применяемыми являются метод нечетких c-средних, алгоритмы Gustafson-Kessel и Gath-Geva, метод нечеткой c-регрессии (FCRM) [4], алгоритм субтрактивной кластеризации [5], а также комбинации указанных методов [6]. Среди перечисленных выше методов FCRM, формирующий форму кластера в виде гиперплоскости, наиболее предпочтителен для решения задач построения нечетких систем типа Такаги-Сугено.…”
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