基于逐点优化模糊推理的模糊控制器及其插值机理

本文从直观优化思想出发,提出一种逐点优化模糊推理方法,称之为POFI方法. 首先分别给出了基于POFI方法的Mamdani蕴涵算子、代数积蕴涵算子和Zadeh蕴涵算子的FMP算法与FMT算法中寻求推理后件(或前件)的计算表达式.然后分别获得了基于POFI方法的Mamdani蕴涵算子模糊控制器、代数积蕴涵算子模糊控制器、Zadeh蕴涵算子模糊控制器及加乘算子模糊控制器的插值表示,由此发现这些模糊控制器具有函数逼近的泛性.其次指出在基于POFI方法的单输入单输出情况下,Mamdani蕴涵算子模糊控制器、代数积蕴涵算子模糊控制器及加乘算子模糊控制器三者相互等效,并且在基于POFI方法的双输入单输出情况下,Mamdani蕴涵算子模糊控制器与代数积蕴涵算子模糊控制器是等效的.     

输入采用广义梯形隶属函数的最简模糊控制器结构分析

总结了应用最为广泛的三角形和梯形隶属函数的共同特点，明确定义了一种将以上两种隶属函数作为特例的广义梯形（GTS）隶属函数，推导了输入空间采用一致GTS（CGTS）模糊分划的两维最简模糊控制器的解析表达式。在此基础上，深入研究了CGTS模糊控制器的解析结构，证明了该模糊控制器等价于一种变结构的线性PI控制器与相应的定常控制偏置之和，并且在其输入论域上是单调递增、连续且有界的，同时揭示了此类控制器是一种更一般化的模糊控制器。     

典型模糊控制器的隶属函数设计及分析

基于一类sum-product模糊推理算子、重心解模糊化法和单点输出的典型模糊控制器的解析结构推导,深入分析了输入隶属函数对模糊控制系统性能的影响,提出了隶属函数的系统化设计方法.并在此基础上,设计了一种具有等差间距因子的三角形隶属函数并推导了其模糊控制器的解析结构,仿真结果证明了该设计的有效性.     

两类模糊系统作为通用逼近器的充分条件的比较与分析

对现有的Mamdani模糊系统和T-S模糊系统作为通用逼近器的充分条件作比较分析,提出了通过三种不同算法获得的单输入单输出Mamdani模糊系统作为通用逼近器的充分条件的保守性比较定理。     

基于输入-输出数据的重心法模糊系统及其泛逼近性

研究基于输入-输出数据的重心法模糊系统及其泛逼近性.首先,将一组单输入-单榆出数据转换成模糊推理规则,通过选择适当的模糊蕴涵算子生成模糊关系,再利用这种模糊关系求出二雄随机变量的联合概率密度函数.当将模糊蕴涵分别取为Larsen蕴涵和Mamdani蕴涵时,分别得到了两种具体的概率密度函数(称之为Larsen分布和Mamdani分布).其次,利用这两种概率分布.分别求出了对应的两种回归函数,指出这种回归函数实际上是模糊控制中的重心法模糊系统.我们分别给出了这种模糊系统具有泛逼近性的充分条件.从而进一步揭示了模糊系统的概率论意义.     

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。     

带积分环节的隶属度函数中心自适应模糊控制器设计

自适应变论域模糊控制是改变模糊控制性能的主要方法之一,针对自适应变论域模糊控制器的稳定性设计这一难题,将输入和输出的隶属度函数分别用论域值表示成解析形式;针对典型模糊控制系统没有考虑积分环节带来的稳态误差问题,设计一种积分器,并从减小控制规则数目的角度出发设计了针对积分环节的控制规则库;引进Lyapunov函数,设计了输入隶属度函数论域值的自适应律和输出隶属度函数中心取值的自适应律,最后针对Duffing非线性系统仿真,结果表明设计的变论域模糊控制器是可行的。     

基于输入-输出数据的概率分布和数字特征

利用模糊推理建立了一种基于输入-输出数据构造联合概率密度函数的方法.首先,将一组单输入-单输出数据转换成模糊推理规则,通过选择适当的模糊蕴涵算子生成模糊关系,再利用这种模糊关系求出二维随机变量的联合概率密度函数.当将模糊蕴涵分别取为Larsen蕴涵和Mamdani蕴涵时,分别得到了两种具体的概率密度函数(称之为Lars...     

双输入-单输出CRI模糊推理方法的还原性

为了深入研究模糊推理的性质,本文研究了双输入-单输出CRI模糊推理方法的还原性.指出:(1)对"∨-∧"合成来说,当模糊蕴涵取为Mamdani和Gdel蕴涵时,双输入-单输出模糊推理具有P_0-还原性;(2)对"∨-∧"合成来说,当模糊蕴涵取为R_0-蕴涵,Zadeh蕴涵和Kleene-Dienes蕴涵时,双输入-单输出模糊推理具有P_1-还原性;(3)对"∨-T"合成来说,当T(a+b)=(a+b-1)∨0,模糊蕴涵为Zadeh蕴涵,Kleene-Dienes蕴涵,Gdel蕴涵,R0蕴涵和Lukasiewicz蕴涵时,双输入-单输出模糊推理具有P_0-还原性.     

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.     

Some aspects of synthesis of probabilistic fuzzy controllers

This paper deals with some aspects of analysis and design problems of a class of control algorithms called probabilistic fuzzy controllers.The notion of random intersection coefficient is proposed here.The decomposition of a multi-dimensional probabilistic fuzzy controller is discussed as well. Such decomposition provides uniform expressions for both single-input, single-output and multi-dimensional probabilistic fuzzy controllers and it improves the computational efficiency.As an example, a two-input and two-output control system is presented as well.     

模糊Delta算子系统的鲁棒镇定

研究一类基于Delta算子描述的T-S模糊模型状态反馈镇定设计问题。首先将全局模糊模型按隶属函数划分成若干子空间,并被表示成不确定系统的形式;采用分段Lyapunov函数法,得到鲁棒稳定化控制律存在的充分条件．该条件被进一步等价表示成一组线性矩阵不等式的可解性问题。克服了以往设计法中需要求解一公共正定矩阵P的不足,也无需求解繁琐的Riccati方程。所得结果可将连续和离散模糊系统的有关结论统一到Delta算子框架内。     

Evaluation of fuzzy linear regression models by comparing membership functions

Fuzzy linear regression models can provide an estimated fuzzy number that has a fuzzy membership function. If a point that has the highest membership value from the estimated fuzzy number is not within the support of the observed fuzzy membership function, a decision-maker can have high risk from the estimate. In this study a modification of fuzzy linear regression analysis based on a criterion of minimizing the difference of the fuzzy membership values between the observed and estimated fuzzy numbers is proposed. Two numerical examples are used to evaluate the fuzzy regression models.     

Neuro-fuzzy technique for navigation of multiple mobile robots

In this paper, navigation techniques for several mobile robots are investigated in a totally unknown environment. In the beginning, Fuzzy logic controllers (FLC) using different membership functions are developed and used to navigate mobile robots. First a fuzzy controller has been used with four types of input members, two types of output members and three parameters each. Next two types of fuzzy controllers have been developed having same input members and output members with five parameters each. Each robot has an array of sensors for measuring the distances of obstacles around it and an image sensor for detecting the bearing of the target. It is found that the FLC having Gaussian membership function is best suitable for navigation of multiple mobile robots. Then a hybrid neuro-fuzzy technique has been designed for the same problem. The neuro-fuzzy technique being used here comprises a neural network, which is acting as a pre processor for a fuzzy controller. The neural network considered for neuro-fuzzy technique is a multi-layer perceptron, with two hidden layers. These techniques have been demonstrated in simulation mode, which depicts that the robots are able to avoid obstacles and reach the targets efficiently. Amongst the techniques developed neuro-fuzzy technique is found to be most efficient for mobile robots navigation. Experimental verifications have been done with the simulation results to prove the authenticity of the developed neuro-fuzzy technique.     

Solving multi-level multi-objective linear programming problems through fuzzy goal programming approach

In this paper, two new algorithms are presented to solve multi-level multi-objective linear programming (ML-MOLP) problems through the fuzzy goal programming (FGP) approach. The membership functions for the defined fuzzy goals of all objective functions at all levels are developed in the model formulation of the problem; so also are the membership functions for vectors of fuzzy goals of the decision variables, controlled by decision makers at the top levels. Then the fuzzy goal programming approach is used to achieve the highest degree of each of the membership goals by minimizing their deviational variables and thereby obtain the most satisfactory solution for all decision makers.     

Editorial

Linear programming problems with fuzzy parameters are formulated by fuzzy functions. The ambiguity considered here is not randomness, but fuzziness which is associated with the lack of a sharp transition from membership to nonmembership. Parameters on constraint and objective functions are given by fuzzy numbers. In this paper, our object is the formulation of a fuzzy linear programming problem to obtain a reasonable solution under consideration of the ambiguity of parameters. This fuzzy linear programming problem with fuzzy numbers can be regarded as a model of decision problems where human estimation is influential.