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基于模糊集合的模糊建模捕述复杂、病态、非线性系统的特性是一种有效方法.文中讨论了从样本数据中通过正交变换和模糊聚类获取模糊规则的方法.利用正交最小二乘对模糊聚类的结果进行变换,采用CGS(Classical Gram—Schmidt)方法确定对建模贡献大的规则,删除对建模贡献小的规则,并对模型中的参数进行估计,能够同时模对糊模型的结构和参数进行辨识.仿真结果表明,提出的方法能够对非线性系统进行模糊建模. 相似文献
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介绍一种基于模糊聚类的模糊辨识方法。首先利用含有聚类准则函数的模糊聚类方法来确定模糊规则数和模型前提参数,然后利用最小二乘法来辨识模型的结论参数,最后采用梯度下降法来调整模型的参数。该方法应用于Box-Jenkins数据仿真实例,仿真结果表明该方法简单有效。 相似文献
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模糊聚类与最小二乘相结合建立非线性系统模型 总被引:1,自引:0,他引:1
提出一种模糊聚类与最小二乘相结合的辨识方法.该方法利用基于模糊似然函数的模糊聚类算法确定系统的模糊划分数目,进而对应聚类个数建立相应的Takagi-Sugeno局部线性化模型,并结合递推最小二乘法,完成系统的辨识.该方法可使模糊模型的结构辨识和参数辨识同时完成,从而实现模糊模型的在线辨识.该方法辨识速度快,精确度高.仿真结果验证了该方法的有效性. 相似文献
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提出了一种新的基于T-S模糊模型的建模方法,首先通过一种局部线性聚类算法,自适应确定模糊规则数目及初始T-S模型的前提和结论参数,建立相应的一阶T-S模糊神经网络.并用梯度下降和递推最小二乘混合算法训练网络参数,从而提高建模精度.最后,通过两个仿真实例验证了本文方法的有效性. 相似文献
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针对现有T-S 模糊模型建模精度与计算效率之间的矛盾, 提出一种利用增广输入变量进行T-S 模糊模型建模的方法. 对输入变量进行多项式增广处理后, 以核模糊?? 均值聚类算法配合聚类评价指标自适应获得最佳聚类数及相应的模糊划分, 并通过递推最小二乘计算得出T-S 模糊模型的后件参数. 提出可利用后件参数反推断前件结构的方法来快速有效地确定前件结构. 最后通过仿真验证了上述方法的有效性.
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基于目标函数的模糊模型一体化建模 总被引:1,自引:0,他引:1
基于模糊集合的模糊模型, 利用模糊推理规则描述复杂、病态、非线性系统是一种有效方法. 本文提出了利用目标函数确定非线性系统的结构和参数的方法. 首先, 通过Gustafson-Kessel(GK)模糊聚类确定模型结构. 然后, 通过目标函数与参数估计一起进行递推计算, 进而实现对模糊模型结构简化, 删除冗余规则. 结构确定过程中采用了UD矩阵分解方法, 大大降低了计算量. 仿真结果证明了提出方法的有效性. 相似文献
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一种模糊辨识方法及其在电站仿真器中的应用 总被引:4,自引:0,他引:4
利用模糊聚类和最小二乘估计方法提出一种糊辨识方法。该方法是基于模糊聚类,计算给定样本在各类中的隶属度,并利用递推最小二乘估计辨识模糊模型的后件参数。采用该方法对火力发电厂电站仿真器中的汽轮发电机密封油冷却系统进行建模研究,取得了满意的效果。 相似文献
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一类基于数据的解释性模糊建模方法的研究 总被引:9,自引:0,他引:9
An approach to identify interpretable fuzzy models from data is proposed. Interpretability, which is one of the most important features of fuzzy models, is analyzed first. The number of fuzzy rules is determined by fuzzy cluster validity indices. A modified fuzzy clustering algorithm,combined with the least square method, is used to identify the initial fuzzy model. An orthogonal least square algorithm and a method of merging similar fuzzy sets are then used to remove the redundancy of the fuzzy model and improve its interpretability. Next, in order to attain high accuracy, while preserving interpretability, a constrained Levenberg-Marquardt method is utilized to optimize the precision of the fuzzy model. Finally, the proposed approach is applied to a PH neutralization process, and the results show its validity. 相似文献
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This paper is concerned with the application of orthogonal transforms and fuzzy competitive learning to extract fuzzy rules from data. The least square algorithm with orthogonal transforms is proposed to supervise the progress of fuzzy competitive learning. First of all, competitive learning takes place in the product space of system inputs and outputs and each cluster corresponds to a fuzzy IF–THEN rule. The fuzzy relation matrix, confirmed by fuzzy competitive learning, is studied by the orthogonal least square algorithm. The validity of fuzzy rules is obtained by analyzing the effect of orthogonal vectors in the fuzzy model, and subsequently removing less important ones. The structure identification and parameter identification of the fuzzy model are simultaneously confirmed in the proposed algorithm. Using simulation results as an example, the fuzzy model of non‐linear systems can be built by using the proposed algorithm. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society 相似文献
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针对基于T-S模糊模型的非线性系统建模问题,提出了一种基于自组织神经网络的新方法.在T-S模糊模型的建模中,目前常用的模糊C均值聚类算法存在迭代次数多,计算耗时的缺点.首先,利用竞争学习算法对输入空间进行聚类,基于此结果,借助于模糊C均值聚类算法进一步优化聚类结果,提取T-S模糊模型的规则前件隶属函数参数.然后,采用最小二乘法求得T-S模糊模型的规则后件参数,从而建立起非线性系统的T-S模糊模型.最后,仿真结果表明,该方法可以为模糊建模提供好的模型结构,并且有较高的计算效率和精度. 相似文献
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为了进一步提高模糊系统建立模型的精度,提出一种新的模糊系统算法ANFIS-HC-QPSO:采用一种混合型模糊聚类算法来对模糊系统的输入空间进行划分,每一个聚类通过高斯函数的拟合产生一个隶属度函数,即完成ANFIS系统的前件参数--隶属度函数参数的初始识别,通过具有量子行为的粒子群算法QPSO与最小二乘法优化前件参数,直至达到停机条件,最终得到ANFIS的前件及后件参数,从而得到满意的模糊系统模型。实验表明,AN-FIS-HC-QPSO算法与传统算法相比,能在只需较少模糊规则的前提下就使模糊系统达到更高的精度。 相似文献
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PieceWise AutoRegressive eXogenous (PWARX) models represent one of the broad classes of the hybrid dynamical systems (HDS). Among many classes of HDS, PWARX model used as an attractive modeling structure due to its equivalence to other classes. This paper presents a novel fuzzy distance weight matrix based parameter identification method for PWARX model. In the first phase of the proposed method estimation for the number of affine submodels present in the HDS is proposed using fuzzy clustering validation based algorithm. For the given set of input–output data points generated by predefined PWARX model fuzzy c-means (FCM) clustering procedure is used to classify the data set according to its affine submodels. The fuzzy distance weight matrix based weighted least squares (WLS) algorithm is proposed to identify the parameters for each PWARX submodel, which minimizes the effect of noise and classification error. In the final phase, fuzzy validity function based model selection method is applied to validate the identified PWARX model. The effectiveness of the proposed method is demonstrated using three benchmark examples. Simulation experiments show validation of the proposed method. 相似文献