共查询到14条相似文献,搜索用时 203 毫秒
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为了提高模糊模型辨识效率,提出了一种新的模糊模型建摸方法,该方法由两步组成:(1)采用基于特征相似性的特征选择方法,去除原始数据的冗余;(2)利用协同模糊聚类与G-K相结合的算法初始化模糊模型,使其前件和后件参数得到优化。采用该算法对有效的特征进行协同模糊聚类,模型参数得到改善,提高了模糊模型辨识的效率。模糊建模的实验结果表明了该方法的有效性。 相似文献
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提出了一种基于减法聚类算法构造解释性模糊模型的方法。首先指出模糊模型解释性的重要地位,分析影响解释性的主要因素;然后利用减法聚类算法辨识初始模糊模型,SVD算法和集合非冗余度约简初始模糊模型,从而提高其解释性;最后采用约束优化算法整体优化模型,提高其精度。PH值中和过程的模糊建模验证了该方法的有效性。 相似文献
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提出一种利用遗传算法进行TS模糊模型的优化设计方法。首先定义了TS模糊模型的精确性指标,给出模糊模型解释性的必要条件。然后利用模糊聚类算法和最小二乘法辨识初始的模糊模型;利用多目标遗传算法优化模糊模型;为提高模型的解释性,在遗传算法中利用基于相似性的模糊集合和模糊规则简化方法对模型进行约简。最后利用该方法进行一类二阶合成非线性动态系统的建模,仿真结果验证了该方法的有效性。 相似文献
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The paper presents a multi-objective genetic approach to design interpretability-oriented fuzzy rule-based classifiers from data. The proposed approach allows us to obtain systems with various levels of compromise between their accuracy and interpretability. During the learning process, parameters of the membership functions, as well as the structure of the classifier's fuzzy rule base (i.e., the number of rules, the number of rule antecedents, etc.) evolve simultaneously using a Pittsburgh-type genetic approach. Since there is no particular coding of fuzzy rule structures in a chromosome (it reduces computational complexity of the algorithm), original crossover and mutation operators, as well as chromosome-repairing technique to directly transform the rules are also proposed. To evaluate both the accuracy and interpretability of the system, two measures are used. The first one – an accuracy measure – is based on the root mean square error of the system's response. The second one – an interpretability measure – is based on the arithmetic mean of three components: (a) the average length of rules (the average number of antecedents used in the rules), (b) the number of active fuzzy sets and (c) the number of active inputs of the system (an active fuzzy set or input means a set or input used by at least one fuzzy rule). Both measures are used as objectives in multi-objective (2-objective in our case) genetic optimization approaches such as well-known SPEA2 and NSGA-II algorithms. Moreover, for the purpose of comparison with several alternative approaches, the experiments are carried out both considering the so-called strong fuzzy partitions (SFPs) of attribute domains and without them. SFPs provide more semantically meaningful solutions, usually at the expense of their accuracy. The operation of the proposed technique in various classification problems is tested with the use of 20 benchmark data sets and compared to 11 alternative classification techniques. The experiments show that the proposed approach generates classifiers of significantly improved interpretability, while still characterized by competitive accuracy. 相似文献
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基于模糊分类的模糊神经网络辨识方法及应用 总被引:2,自引:6,他引:2
基于改进的T-S模型,提出一种自适应模糊神经网络模型(AFNN),给出了网络的连接结构和学习算法。基于竞争学习算法的模糊分类器确定系统的模糊空间和模糊规则数,并得出每个样本对每条规则的适用程度。利用卡尔曼滤波算法在线辨识删的后件参数。AFNN结构简洁,逼近能力强,能够显著提高辨识精度,并且在线辨识的模糊模型简单有效。将该AFNN用于非线性系统的模糊辨识和化工过程连续搅拌反应器(CSTR)的建模中,仿真结果验证了该方法的有效性,表明该网络能够实现复杂非线性系统的建模,而且建模精度高、收敛速度快。可当作复杂系统建模的一种有效手段。 相似文献