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本文研究了前提不匹配的Tagaki-Sugeno(T–S)模糊时滞系统的镇定问题.与一般的T–S模糊时滞系统相比,该系统中模糊模型与模糊控制器拥有不同的模糊规则数与不同的隶属度函数.基于Lyapunov稳定性理论,通过引进新型积分不等式,给出了包含隶属度函数信息的镇定条件.本文提出的新方法充分考虑了隶属度函数的信息,同时得到了Lyapunov函数导数的最小下界,因此新的镇定条件比以往结果具有更小的保守性.另一方面给出前提不匹配的控制器设计方法,由于模糊控制器的隶属度函数可以任意选取,因此提高了控制器设计的灵活性.最后仿真实例证明了本文方法的有效性及优越性. 相似文献
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为剖析一般齐次T-S模糊系统的逼近性能,通过广泛总结常用模糊集的特点,明确定义了一种具有普遍意义的输入空间的一般模糊划分(GFP).基于输入采用GFP的一般齐次T-S模糊系统的解析结构,证明了该类一般齐次T-S模糊系统能够以任意精度逼近任意非线性函数,并得到了一个其作为通用逼近器的充分条件.作为GFP的一种退化,进一步研究了输入采用线性模糊划分(LFP)的一般齐次T-S模糊系统的一阶逼近性能.仿真实例验证了所得理论结果的有效性,并考察了充分条件的保守性.这为基于齐次T S模糊模型的复杂系统建模与控制提供了理论指导. 相似文献
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研究模糊控制器问题,针对广泛应用的三角形和梯形隶属函数存在的问题,为提高控制器的控制性能,设计并研究了一种将三角形和梯形隶属函数作为特例的广义线性隶属函数,并将它作为输入隶属函数,应用于两输入一输出的典型模糊控制器.基于模糊控制器的解析结构,在不增加模糊规则的情况下,增加模糊子空间的划分,使用合适的隶属函数提高模糊控制器的控制性能;同时,证明了输入为广义线性隶属函数的模糊控制系统的解析结构是全局多值继电器和局部PI控制器之和.最后,仿真结果证明了设计的有效性,并为线性隶属函数的模糊控制器起到了优化性能的作用. 相似文献
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研究目的旨在建立智能机器人机械臂总体动态可靠性的评价方法,根据智能机器人机械臂各运动参数的概率信息,利用模糊方法,确定单因素的隶属度函数,按照运动参数可靠度进行单因素模糊评价,得到不同构件的可靠度评价;然后根据粗糙集理论判断各个构件对机械臂总体可靠性的影响,对概率信息进行约简;再根据不同构件的可靠度进行多因素模糊综合评价,得到整个智能机器人机械臂的总体可靠度的评价。通过算例证明该方法对复杂智能机器人机械臂总体可靠性评价是有效可行的。 相似文献
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针对SISO非仿射非线性系统,提出一种新型自主构架模糊控制器.此控制器由鲁棒控制器与自主构架模糊系统构成.模糊系统初始只含有一条规则,根据系统误差和ε完备性2条准则自主增加规则及隶属函数,从而完善模糊系统结构,逼近非线性系统不确定量.模糊系统利用"伪模糊输出"法对新增规则后件初始化,考虑到实际计算能力,采用替换隶属函数机制限制规则数目.通过理论推导证明了系统的稳定性,理论和半实物仿真实验验证了所提出方法的有效性. 相似文献
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一种新型区间二型模糊神经网络隶属函数的设计 总被引:1,自引:0,他引:1
Wang Jiajun 《自动化学报》2017,43(8):1425-1433
对于区间二型模糊神经网络(IT2FNN),论文给出了一种新型的模糊隶属函数(FMF)设计方法.通过所设计的模糊隶属函数,可以衍生出三种区间二型模糊隶属函数(IT2FMF).每种区间二型模糊隶属函数都具有不同的不确定域.论文将三种衍生模糊隶属函数应用于简化区间二型模糊神经网络辨识两个非线性系统.通过仿真,将衍生区间二型模糊隶属函数的辨识性能与高斯和椭圆型模糊隶属函数进行了对比.仿真结果表明,通过调节简化区间二型模糊神经网络的参数,本文所设计的区间二型模糊隶属函数比高斯和椭圆型模糊隶属函数具有更好的辨识性能. 相似文献
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This paper presents a new method for fuzzy system reliability analysis based on fuzzy time series and the alpha-cuts arithmetic operations of fuzzy numbers, where we allow the reliabilities of the components of a system at different times t (t= ...,0,1,2,...) to be represented by different membership functions. Because the proposed method allows the reliabilities of the components of a system at different times t to have different membership functions, it is more flexible than the ones presented in Chen 1994 , Cheng and Mon 1993 , and Singer 1990 . Furthermore, because the proposed method uses the simple alpha -cuts arithmetic operations of fuzzy numbers rather than the complicated nonlinear programming techniques mentioned in Mon and Cheng 1994 , it is simpler in calculating fuzzy system reliability than the one presented in that paper. 相似文献
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探讨一类高效率Mamdani模糊系统隶属函数优化方法.首先通过严密的理论分析将MISO(多输入单输出)_Mamdani模糊系统的输入/输出函数表示成系统隶属函数的局部线性表达式;论证了这个表达式中系统隶属函数项的系数仅由该点所对应的2p个隶属函数值,按大小排成的序列决定.以此为基础,提出了根据输入/输出样本集误差对系统隶属函数进行优化的新方法.该方法近似地将隶属函数优化问题转换成一组线性规划问题进行求解.本文提供的仿真结果也进一步证实了该方法的有效性. 相似文献
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Suad Smail Halil?evi? Ferdinand Gubina 《Engineering Applications of Artificial Intelligence》2011,24(6):1026-1034
This paper describes a method for fuzzy calculation of power system reliability. It introduces a composite fuzzy security and fuzzy adequacy indices encompassing the power system segments of generation, transmission, and the segment of load nodes. The fuzzy security index is determined by the security membership grades of the power system components which are found to be the worst in their segments. The fuzzy adequacy index is based on the adequacy indices of generation, transmission, and transmission integration segments. The composite fuzzy reliability index enables the system operator to have a continuous insight into the distance between the power system's actual state and the closest unreliable state by tracking the system's components with the lowest security and adequacy membership grades of the respective segments. The algorithm has been tested on the models of the IEEE Three Area RTS-96 and Bosnia-Herzegovina power systems. 相似文献
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Roelof K. Brouwer Albert Groenwold 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2009,13(6):577-589
The first stage of knowledge acquisition and reduction of complexity concerning a group of entities is to partition or divide
the entities into groups or clusters based on their attributes or characteristics. Clustering algorithms normally require
both a method of measuring proximity between patterns and prototypes and a method for aggregating patterns. However sometimes
feature vectors or patterns may not be available for objects and only the proximities between the objects are known. Even
if feature vectors are available some of the features may not be numeric and it may not be possible to find a satisfactory
method of aggregating patterns for the purpose of determining prototypes. Clustering of objects however can be performed on
the basis of data describing the objects in terms of feature vectors or on the basis of relational data. The relational data
is in terms of proximities between objects. Clustering of objects on the basis of relational data rather than individual object
data is called relational clustering. The premise of this paper is that the proximities between the membership vectors, which
are obtained as the objective of clustering, should be proportional to the proximities between the objects. The values of
the components of the membership vector corresponding to an object are the membership degrees of the object in the various
clusters. The membership vector is just a type of feature vector. Based on this premise, this paper describes another fuzzy
relational clustering method for finding a fuzzy membership matrix. The method involves solving a rather challenging optimization
problem, since the objective function has many local minima. This makes the use of a global optimization method such as particle
swarm optimization (PSO) attractive for determining the membership matrix for the clustering. To minimize computational effort,
a Bayesian stopping criterion is used in combination with a multi-start strategy for the PSO. Other relational clustering
methods generally find local optimum of their objective function. 相似文献
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This paper reports on a related study on approximation theory of fuzzy systems. First, some basic principles are presented to construct membership functions. Then, an approach is proposed to form membership functions by using translations and dilations of one fixed function (called a basis function) which is very similar to that in wavelets analysis. The properties of this type of membership function reflect the advantages of the given approach. Finally, it is proved that fuzzy systems based on such membership functions are universal approximators under certain mild conditions on the basis function. This conclusion expands the family of fuzzy systems which can be universal approximators 相似文献
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Mohit Kumar Shiv Prasad Yadav Surendra Kumar 《International journal of systems science》2013,44(1):50-66
In general, fuzzy sets are used to analyse the system reliability. In this article, the concept of fuzzy set is extended by the idea of intuitionistic fuzzy set (IFS) and a new general procedure is proposed to construct the membership and non-membership functions of the fuzzy reliability using time-dependent IFS. Here, failure rate function of the system is represented by a triangular intuitionistic fuzzy number (IFN). Also, using proposed approach, membership and non-membership functions of fuzzy reliability of series and parallel systems are constructed, where the failure rate of each component is taken as a time-dependent triangular IFN. The major advantage of using IFS over fuzzy sets is that IFS separate the positive and negative evidences for membership of an element in the set. Numerical examples are given to illustrate the proposed approach. 相似文献
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This research addresses system reliability analysis using weakest t-norm based approximate intuitionistic fuzzy arithmetic operations, where failure probabilities of all components are represented by different types of intuitionistic fuzzy numbers. Due to the incomplete, imprecise, vague and conflicting information about the component of system, the present study evaluates the reliability of system in terms of membership function and non-membership function by using weakest t-norm (Tw) based approximate intuitionistic fuzzy arithmetic operations on different types of intuitionistic fuzzy numbers. In general, interval arithmetic (α-cut arithmetic) operations have been used to analyze the fuzzy system reliability. In complicated systems, interval arithmetic operations may occur the accumulating phenomenon of fuzziness. In order to overcome the accumulating phenomenon of fuzziness, this research adopts approximate intuitionistic fuzzy arithmetic operations under the weakest t-norm arithmetic operations (Tw) to analyze fuzzy system reliability. The approximate intuitionistic fuzzy arithmetic operations employ principle of interval arithmetic under the weakest t-norm arithmetic operations. The proposed novel fuzzy arithmetic operations may obtain fitter decision values, which have smaller fuzziness accumulating and successfully analyze the system reliability. Also weakest t-norm arithmetic operations provide more exact fuzzy results and effectively reduce fuzzy spreads (fuzzy intervals). Using proposed approach, fuzzy reliability of series system and parallel system are also constructed. For numerical verification of proposed approach, a malfunction of printed circuit board assembly (PCBA) is presented as a numerical example. The result of the proposed method is compared with the listing approaches of reliability analysis methods. 相似文献
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Fuzzy basis functions, universal approximation, and orthogonalleast-squares learning 总被引:63,自引:0,他引:63
Fuzzy systems are represented as series expansions of fuzzy basis functions which are algebraic superpositions of fuzzy membership functions. Using the Stone-Weierstrass theorem, it is proved that linear combinations of the fuzzy basis functions are capable of uniformly approximating any real continuous function on a compact set to arbitrary accuracy. Based on the fuzzy basis function representations, an orthogonal least-squares (OLS) learning algorithm is developed for designing fuzzy systems based on given input-output pairs; then, the OLS algorithm is used to select significant fuzzy basis functions which are used to construct the final fuzzy system. The fuzzy basis function expansion is used to approximate a controller for the nonlinear ball and beam system, and the simulation results show that the control performance is improved by incorporating some common-sense fuzzy control rules. 相似文献
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A. Chatterjee A. Rakshit P. Siarry 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2006,10(8):631-642
The present paper is a humble attempt to develop a fuzzy function approximator which can completely self-generate its fuzzy
rule base and input-output membership functions from an input-output data set. The fuzzy system can be further adapted to
modify its rule base and output membership functions to provide satisfactory performance. This proposed scheme, called generalised
influential rule search scheme, has been successfully implemented to develop pure fuzzy function approximators as well as
fuzzy logic controllers. The satisfactory performance of the proposed scheme is amply demonstrated by implementing it to develop
different major components in a process control loop. The versatility of the algorithm is further proved by implementing it
for a benchmark nonlinear function approximation problem. 相似文献