| 一种T-S模糊模型的自组织辨识算法及应用 |
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| 引用本文： | 梁炎明,刘丁.一种T-S模糊模型的自组织辨识算法及应用[J].仪器仪表学报,2011,32(9). |
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| 作者姓名： | 梁炎明 刘丁 |
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| 作者单位： | 西安理工大学自动化与信息工程学院 西安710048 |
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| 基金项目： | 国家科技重大专项(No.2009ZX02011001)资助项目 |
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| 摘 要： | 提出了一种新的具有自适应学习能力的T-S模糊模型辨识算法.该算法通过使同一条规则的高斯函数的宽度参数彼此相等建立与支持向量机等效的T-S模糊模型,在此基础上,利用模糊聚类算法和支持向量机分别建立前后件辨识模型,并利用一种改进粒子群优化算法优化输出误差函数使前后件参数联合辨识,从而获得T-S模糊模型的结构和参数.仿真结果表明,相比其它方法,文中方法具有较高的逼近精度和较好的泛化能力,由此算法获得的直拉单晶炉热场模型具有0.1171的均方差,完全符合均方差小于0.5的要求.
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| 关 键 词： | T-S模糊模型 自组织 支持向量机 模糊聚类 粒子群优化 直拉单晶炉热场 |
Self-organizing identification algorithm for T-S fuzzy model and its applications |
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| Liang Yanming,Liu Ding.Self-organizing identification algorithm for T-S fuzzy model and its applications[J].Chinese Journal of Scientific Instrument,2011,32(9). |
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| Authors: | Liang Yanming Liu Ding |
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| Affiliation: | Liang Yanming,Liu Ding(School of Automation & Information Engineering,Xi'an University of Technology,Xi'an 710048,China) |
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| Abstract: | A new identification algorithm for T-S fuzzy model has been proposed,which has the properties of self-learning and self-adapting.This algorithm establishes a T-S fuzzy model equivalent to support vector machine(SVM) model through making Gaussian function width parameters in the same rule equal each other.Based on this,the antecedent identification model and the consequent identification model are respectively established using fuzzy clustering algorithm and support vector machine(SVM).An improved particle s... |
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| Keywords: | T-S fuzzy model self-organizing support vector machine fuzzy clustering Particle Swarm Optimization hot-zone of Czochralski crystal growing furnace |
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