模糊蕴涵算子的后件单调性及其构造的模糊系统的数学表示

根据模糊蕴涵算子θ(a,b)关于后件变量b的单调性,将文献中的400多个蕴涵算子分为三类,即后件单增(减)和后件非单调模糊蕴涵算子.进一步,给出了不同类型的蕴涵算子构造的模糊系统的数学表达式.结果表明:若后件单增蕴涵算子θ(a,b)满足θ(a,1)=φ(a)或后件单残蕴涵算子θ(a,b)满足φ(a,0)=(a)(其中φ(a)为关于a的函数.且当0相似文献   

基于后件模糊集中心的Mamdani模糊系统的降维分解

模糊系统是通过模糊规则来处理不确定信息的一种重要工具,而Mamdani模糊系统则是最简单的一类高维系统模型.文章首先从理论上指出Mamdani模糊系统的传统分解存在错误,并分别针对二维和三维Mamdani模糊系统的实际分解公式进行了分析.其次,基于后件(输出)模糊集中心指标重新获得了n维Mamdani模糊系统的降维分解公式.最后,通过实例分析说明该分解公式不仅可以降低高维Mamdani模糊系统的空间维数,而且也可减少系统内部的模糊规则总数.     

由后件单增模糊蕴涵算子构造的一类模糊系统及其逼近特性

给出由关于规则后件单增的模糊蕴涵算子构造的乘积推理机、"单点"模糊化方法和中心平均解模糊化方法设计的模糊系统, 并分析了它对紧集上连续可微函数的逼近特性.结果表明: 当模糊蕴涵算子θ满足θ(a,1)=1时, 模糊系统不具有逼近能力; 当θ(a,1)=p(a)(当0相似文献   

基于三角形和高斯模糊化的Mamdani模糊系统表示

模糊化是将模糊系统中输入变量的确定值转换为相应模糊集合的过程,它在模糊系统建模和模糊控制领域有着重要的作用。本文在前件模糊集取为三角形模糊数条件下,利用函数极值方法求解后件模糊集的隶属函数,进而给出基于三角形模糊化和高斯模糊化的两种Mamdani模糊系统表示。     

基于模糊相似度的广义Mamdani模糊系统及其逼近

模糊相似度是通过局部信息来刻画两个模糊集相似程度的度量,它是进一步研究模糊控制的重要理论工具。本文基于模糊相似度重新给出后件模糊集的计算公式,进而依据广义模糊化、乘积推理机和中心平均解模糊化获得多输入单输出广义Mamdani模糊系统模型及其表示,并通过实例得到该模糊系统的表达式.此外,利用多元微分中值定理证明了广义Mamdani模糊系统对连续可微函数具有一阶逼近性。     

一类变论域自适应模糊控制器

目前常用的自适应模糊控制器存在控制精度不高和调整时间长等问题.变论域自适应模糊控制器能够显著提高控制精度并缩短其调整时间,但其控制效果对一组完备的初始模糊规则的依赖,限制了其应用的范围.针对上述问题,本文提出了一种推理规则前件论域可变,后件峰点自适应的自适应模糊控制器.Lyapnuov综合分析法证明新的控制器具有全局稳定性,并通过仿真实验表明,新控制器不仅能够提高控制精度,而且较为合理地回避了对初始模糊规则的依赖性,扩展了变论域模糊控制的适用范围.     

基于生物适应对策的间接自适应模糊控制

针对一类不确定非线性系统,提出了一种基于生物适应对策的间接自适应模糊控制方法.方法在控制器的设计中,将生态位态势理论函数作为模糊规则的后件构造模糊系统,给出了基于生物适应对策的自适应控制器.控制器的设计既体现了生物对环境的适应性,又体现了生物开发和利用环境的能力.通过实例仿真说明,控制器与常规控制器相比,具有更好的控制效果,而且具有良好的抗干扰性.     

基于网格分片线性函数构造的非齐次线性T-S模糊系统的逼近性分析

分片线性函数是一元分段线性函数在多元情况下的推广,它在沟通模糊系统和被逼近函数关系中起着重要的桥梁作用.文章基于多元连续函数的网格分片线性函数(grid piecewise linear function,GPLF)重新构造了非齐次线性T-S模糊系统,并依据行列式性质和矩阵模证明了当规则后件线性部分所有参数选取非零常数时该系统对GPLF也具有逼近性.进而在最大模意义下获得该线性T-S模糊系统对连续函数类构成逼近器.此外,通过模拟实例对非齐次线性T-S模糊系统进行逼近精度分析.结果显示,该非齐次线性T-S模糊系统可按任意精度逼近所给连续函数.     

基于关联的区间犹豫模糊PROMETHEE多属性决策方法

针对属性模糊测度已知、属性值为区间犹豫模糊集的决策问题,提出了一种属性关联的PROMETHEE多属性决策方法.首先引入得分函数,构造区间犹豫模糊决策矩阵;通过区间可能度函数的概念,构造正弦属性偏好函数;进一步,结合A模糊测度与Choquet积分,计算方案的优先指数;进而计算方案的流出、流入以及净流值,并根据各方案的净流值的大小进行排序.最后通过实例分析说明了该方法的有效性和合理性.     

基于生物态势理论的Backstepping模糊控制

针对一类单输入单输出非线性系统,提出了一种基于生物态势理论的backstepping模糊自适应控制方法.设计中,将系统误差及其导数作为模糊规则的前件,将反应生物特性的生态位态势理论函数作为模糊规则的后件,设计了基于生物态势理论的Backstepping模糊控制器.该控制器将生物个体对外界扰动的适应对策引入设计中,使得控制器的自适应律具有生物自适应特性.并利用Lyapunov方法证明了闭环系统的稳定性.仿真结果进一步验证了方法的有效性.     

Multifactorial fuzzy sets and multifactorial degree of nearness

Two new concepts, Multifactorial fuzzy sets and multifactorial degree of nearness, are advanced. First, an axiomatic definition for multifactorial functions is given, and multifactorial fuzzy sets and multifactorial degree of nearness are defined by using the multifactorial function. Second, three direct methods of multifactorial pattern recognition and two methods for fuzzy clustering with fuzzy characteristics are given. Last, we advance an interesting open problem.     

New fuzzy wavelet network for modeling and control: The modeling approach

In this paper, a fuzzy wavelet network is proposed to approximate arbitrary nonlinear functions based on the theory of multiresolution analysis (MRA) of wavelet transform and fuzzy concepts. The presented network combines TSK fuzzy models with wavelet transform and ROLS learning algorithm while still preserve the property of linearity in parameters. In order to reduce the number of fuzzy rules, fuzzy clustering is invoked. In the clustering algorithm, those wavelets that are closer to each other in the sense of the Euclidean norm are placed in a group and are used in the consequent part of a fuzzy rule. Antecedent parts of the rules are Gaussian membership functions. Determination of the deviation parameter is performed with the help of gold partition method. Here, mean of each function is derived by averaging center of all wavelets that are related to that particular rule. The overall developed fuzzy wavelet network is called fuzzy wave-net and simulation results show superior performance over previous networks.The present work is complemented by a second part which focuses on the control aspects and to be published in this journal([17]). This paper proposes an observer based self-structuring robust adaptive fuzzy wave-net (FWN) controller for a class of nonlinear uncertain multi-input multi-output systems.     

Multidimensional interval-valued fuzzy reasoning approach based on weighted similarity measure

This paper focuses on presentation of a method to bidirectional interval-valued fuzzy approximate reasoning by employing a weighted similarity measure between the fact and the antecedent (or consequent) portion of production rule in which the vague terms are represented by interval-valued fuzzy concepts rather than plain fuzzy sets. The proposed method is more reasonable and flexible than the one presented in the paper by Chen [Fuzzy Sets and Systems, 91(1997), 339–353] due to the fact that it not only can deal with multidimensional interval-valued fuzzy reasoning scheme, but also consider the different importance degree of linguistic variables in production rule and that of elements in each universe.     

Fuzzy control of original UPOs of unknown discrete chaotic systems

This paper presents a fuzzy algorithm for controlling original unstable periodic orbits of unknown discrete chaotic systems. In the modeling phase, only input–output data pairs provided from the true system are required. The fuzzy model is developed using Gaussian membership functions and consequent functions where the Levenberg–Marquardt computational algorithm is employed for the model parameters calculation. In the controller design phase, the L₂-stability criterion is used, which forms the basis of the main design principle. Simulation results are given to illustrate the effectiveness and control performance of the proposed method.     

Using multiple sources of information to recognize and classify objects

In this paper we consider the problem of selecting an object or a course of action from a set of possible alternatives. To give the paper focus, we concentrate initially on an object recognition problem in which the characteristic features of the object are reported by remote sensors. We then extend the method to a more general class of selection problems and consider several different scenarios.

Information is provided by a set of knowledge system reports on a single feature, and the output from these systems is not totally explicit but provides posible values for the observed feature along with a degree of certitude.We use fuzzy sets to represent this vague information. Information from independent sources is combined using the Dempster-Shafer approach adapted to the situation in which the focal elements are fuzzy as in the recent paper by J. Yen [7]. We base our selection rule on the belief and plausibility functions generated by this approach to accessing evidence.

For situations in which the information is too sparse and/or too vague to make a single selection, we construct a preference relationship based on the concept of averaged subsethood for fuzzy sets as discussed by B. Koskoin [4]. We also define an explicit metric upon which to base our selection mechanism for situations in which the Dempster-Shafer rule of combination is inappropriate     

Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework

In this paper we propose a multi-objective evolutionary algorithm to generate Mamdani fuzzy rule-based systems with different good trade-offs between complexity and accuracy. The main novelty of the algorithm is that both rule base and granularity of the uniform partitions defined on the input and output variables are learned concurrently. To this aim, we introduce the concepts of virtual and concrete rule bases: the former is defined on linguistic variables, all partitioned with a fixed maximum number of fuzzy sets, while the latter takes into account, for each variable, a number of fuzzy sets as determined by the specific partition granularity of that variable. We exploit a chromosome composed of two parts, which codify the variables partition granularities, and the virtual rule base, respectively. Genetic operators manage virtual rule bases, whereas fitness evaluation relies on an appropriate mapping strategy between virtual and concrete rule bases. The algorithm has been tested on two real-world regression problems showing very promising results.     

Adaptive fuzzy output feedback control for a class of uncertain nonlinear systems with unknown backlash-like hysteresis

An output feedback controller is proposed for a class of uncertain nonlinear systems preceded by unknown backlash-like hysteresis, where the hysteresis is modeled by a differential equation. The unknown nonlinear functions are approximated by fuzzy systems based on universal approximation theorem, where both the premise and the consequent parts of the fuzzy rules are tuned with adaptive schemes. The proposed approach does not need the availability of the states, which is essential in practice, and uses an observer to estimate the states. An adaptive robust structure is used to cope with lumped uncertainties generated by state estimation error, approximation error of fuzzy systems and external disturbances. Due to its adaptive structure the bound of lumped uncertainties does not need to be known and at the same time the chattering is attenuated effectively. The strictly positive real (SPR) Lyapunov synthesis approach is used to guarantee asymptotic stability of the closed-loop system. In order to show the effectiveness of the proposed method simulation results are illustrated.     

Piecewise parametric polynomial fuzzy sets

We present a scheme for tractable parametric representation of fuzzy set membership functions based on the use of a recursive monotonic hierarchy that yields different polynomial functions with different orders. Polynomials of the first order were found to be simple bivalent sets, while the second order polynomials represent the typical saw shape triangles. Higher order polynomials present more diverse membership shapes. The approach demonstrates an enhanced method to manage and fit the profile of membership functions through the access to the polynomials order, the number and the multiplicity of anchor points as wells as the uniformity and periodicity features used in the approach. These parameters provide an interesting means to assist in fitting a fuzzy controller according to system requirements. Besides, the polynomial fuzzy sets have tractable characteristics concerning the continuity and differentiability that depend on the order of the polynomials. Higher order polynomials can be differentiated as many times as the order of the polynomial less the multiplicity of the anchor points. An algorithmic optimization approach using the steepest descent method is introduced for fuzzy controller tuning. It was shown that the controller can be optimized to model a certain output within small number of iterations and very small error margins. The mathematics generated by the approach is consistent and can be simply generalized to standard applications. The recursive propagation was noticed for its clarity and ease of calculations. Further, the degree of association between the sets is not limited to the neighbors as in traditional applications; instead, it may extend beyond.Such approach can be useful in dynamic fuzzy sets for adaptive modeling in view of the fact that the shape parameters can be easily altered to get different profiles while keeping the math unchanged. Hypothetically, any shape of membership functions under the partition of unity constraint can be produced. The significance of the mentioned characteristics of such modeling can be observed in the field of combinatorial and continuous parameter optimization, automated tuning, optimal fuzzy control, fuzzy-neural control, membership function fitting, adaptive modeling, and many other fields that require customized as well as standard fuzzy membership functions. Experimental work of different scenarios with diverse fuzzy rules and polynomial sets has been conducted to verify and validate our results.     

Nonsingular decoupled terminal sliding-mode control for a class of fourth-order nonlinear systems

This paper presents a nonsingular decoupled terminal sliding mode control (NDTSMC) method for a class of fourth-order nonlinear systems. First, the nonlinear fourth-order system is decoupled into two second-order subsystems which are referred to as the primary and secondary subsystems. The sliding surface of each subsystem was designed by utilizing time-varying coefficients which are computed by linear functions derived from the input–output mapping of the one-dimensional fuzzy rule base. Then, the control target of the secondary subsystem was embedded to the primary subsystem by the help of an intermediate signal. Thereafter, a nonsingular terminal sliding mode control (NTSMC) method was utilized to make both subsystems converge to their equilibrium points in finite time. The simulation results on the inverted pendulum system are given to show the effectiveness of the proposed method. It is seen that the proposed method exhibits a considerable improvement in terms of a faster dynamic response and lower IAE and ITAE values as compared with the existing decoupled control methods.     

Exact inversion of decomposable interval type-2 fuzzy logic systems

It has been demonstrated that type-2 fuzzy logic systems are much more powerful tools than ordinary (type-1) fuzzy logic systems to represent highly nonlinear and/or uncertain systems. As a consequence, type-2 fuzzy logic systems have been applied in various areas especially in control system design and modelling. In this study, an exact inversion methodology is developed for decomposable interval type-2 fuzzy logic system. In this context, the decomposition property is extended and generalized to interval type-2 fuzzy logic sets. Based on this property, the interval type-2 fuzzy logic system is decomposed into several interval type-2 fuzzy logic subsystems under a certain condition on the input space of the fuzzy logic system. Then, the analytical formulation of the inverse interval type-2 fuzzy logic subsystem output is explicitly driven for certain switching points of the Karnik–Mendel type reduction method. The proposed exact inversion methodology driven for the interval type-2 fuzzy logic subsystem is generalized to the overall interval type-2 fuzzy logic system via the decomposition property. In order to demonstrate the feasibility of the proposed methodology, a simulation study is given where the beneficial sides of the proposed exact inversion methodology are shown clearly.