模糊推理的程序化

在模糊控制过程中,普遍采用CRI模糊推理方法,但由于其计算量大,阻碍了模糊控制的应用。在Mathematica环境之下,巧妙地运用Mathematica语言的特点及处理矩阵自定义运算的能力,将模糊推理程序化。     

一种基于柔性逻辑的控制方法研究

模糊控制是基于领域专家所给出的模糊控制规则来实现对系统的控制,这些模糊控制规则粗略地描述了控制器输入和输出之间的关系.模糊控制采用的是一种分段逼近的思想,因此在对高阶和多输入等实际复杂系统控制过程中,模糊控制存在控制规则组合爆炸和控制精度不高两大问题.从常规二维模糊控制器的输入变量误差E和误差变化率EC的基本物理意义出发,深入分析它们之间所蕴含的逻辑关系,指出这种关系的本质就是泛逻辑学中的泛组合关系,可用简单的泛组合运算代替复杂的模糊规则推理过程.据此提出了一种柔性逻辑控制方法,可实现对复杂系统的精确控制.最后,一级倒立摆的实物实验结果证明了该方法的可行性和有效性.     

基于遗传算法的非线性PID调节器的设计

0　引言长期以来 ,传统的ＰＩＤ调节器在控制工程中占主导地位。然而 ,在实际应用中 ,对动态性能要求高的系统 ,常规线性ＰＩＤ调节器很难达到设计目的。文献 [1]提出一种带优化修正函数的规则自整定模糊控制算法 ,它采用调节器参数寻优的方法 ,可使模糊控制规则自整定 ,但它不能从本质上消除量化误差、静差与Ｇｉｂｂｓ现象[2 ] 。本文在文献 [1]调节器结构的基础上进行了改进 ,取消了模糊控制的量化取整运算与推理合成运算 ,提出一种带优化修正函数的非线性ＰＩＤ调节器的设计方法 ,将参数设计问题转化为离线寻优问题 ,并借助遗传算法 ,较…     

快速取模算法在BLACKFIN DSP上的实现

针对目前在DSP上对傅里叶变换结果进行取模运算常用的查表法需要的存储量很大,近似计算法速度慢等不足。设计了一种综合近似计算法和查表法的取模运算算法,通过误差分析确定表的最优存储量,编制了在BLACKFIN系列DSP上对小数取模运算的程序。实验表明,该方法在占用极少量存储空间的条件下使得取模运算速度为近似计算法的2.03倍,并且精度提高了1位。     

基于模糊补偿的机械手鲁棒自适应模糊控制研究

多关节机械手系统中普遍存在摩擦特性、随机干扰及负载变化等非线性因素的影响。针对传统的PID控制和模糊控制很难对该类系统实现快速高精度的跟踪控制等问题,本文在模糊信息已知并且所有状态变量均可测得的情况下,设计了一种基于模糊补偿的鲁棒自适应模糊控制律。同时,为了减少模糊逼近的计算量,提高运算效率,采用了对不同的扰动补偿项加以区分、分别逼近的方法。仿真实验结果表明,这种改进的带模糊补偿的鲁棒自适应模糊控制可以很好地抑制摩擦、扰动及负载变化等非线性因素的影响。     

基于视线的智能碰撞检测方法

针对在虚拟漫游系统中运动物体的碰撞检测问题,提出一种基于视线的智能碰撞检测方法。分析虚拟现实漫游环境中碰撞检测的基本原理和基于视线的碰撞检测,采用模糊控制隶属函数提高碰撞检测效率。实验结果表明,该方法的运算效率和检测精度有所提高,能避开墙体和障碍物。     

模糊推理的程序化

在模糊控制过程中，采用ＣＲＩ模糊推理方法，但由于其计算量大，阻碍了模糊控制的应用。在Ｍａｔｈｅｍａｔｉｃａ环境之下，巧妙地运用Ｍａｔｈｅｍａｔｉｃａ语言的特点及处理矩阵自定义运算的能力，将模糊揄程序化．     

Matlab的动态数据交换及其应用研究

为了充分利用Matlab强大的矩阵运算能力及其丰富的工具箱,对Matlab的动态数据交换进行了研究,通过动态数据交换完成Matlab与其他应用程序间的通信,从而为Matlab在实时控制中的应用奠定了基础。将Matlab模糊控制工具箱开发出来的模糊控制算法用于电气加热炉的控制,控制效果非常好。     

基于作用模糊子集推理的单片机模糊控制实现原理及其应用

针对DCRI模糊推理方法的复杂性,首先提出了作用模糊子集推理方法;然后将该推理方法与单片机数字运算少年叮结合,提出了基于作用模糊子集推理的单片机模糊控制实现原理,研制了开发了80C552型单片机模糊控制器;最后以建筑热工系统为被控对象,试验研究了测试室温度模糊控制过程。     

符号型自适应模糊控制器

伸缩因子设计是变论域模糊控制的关键, 也是设计的难点. 借助于Lyapunov综合分析方法, 提出一种符号型自适应模糊控制方案, 避免花费精力设计伸缩因子. 方案中使用符号函数替代输入的伸缩运算, 后件调整仍使用积分调节因子. 因此, 本质上它仍是一种论域可变的模糊控制. 相比较变论域模糊控制, 该方案所需规则少, 稳态精度高, 鲁棒性好. Lyapunov稳定性理论保证了跟踪误差的渐近收敛. 最后, 实例证实了方案的可行性.     

直觉模糊逻辑算子研究’

模糊逻辑算子是模糊信息融合、模糊推理及模糊决策的重要工具。针对作为模糊逻辑算子重要扩展的直觉模糊逻辑算子,首先引入了直觉模糊集在特殊格上的等价定义。其次,验证了直觉模糊t-模与s-模的若干重要性质。在此基础上,对两种常用的蕴涵算子：直觉模糊孓蕴涵与直觉模糊R-蕴涵所具有的新性质进行讨论和证明,从而便于直觉模糊逻辑算子的进一步应用。     

复合模糊命题中基于弱逻辑的拟三角模算子研究

修正了f范数的概念,指出了符合弱逻辑关系的算子实际上是一种拟三角模算子中的Uninorm算子,接着给出了严格弱逻辑关系的拟三角模算子概念,在提出概念时,考虑了多维、基于单一数值和基于区间值以及加权的情况,并证明了符合弱逻辑关系的连续拟三角模算子是不存在的;给出了弱逻辑拟三角模算子的具体形式,给出了具体的四类弱逻辑关系的拟三角模算子,讨论了它们的性质,并进行了比较;定义了评价算子的边缘性测度和敏感性测度,对各算子进行了对比和评价.结果表明,文中给出的弱逻辑拟三角模算子可以在不同应用背景下,有效地处理不同类型的复合模糊命题真值运算,也可以在其它的模糊系统中有效处理多个模糊子集之间的聚集运算.     

Implication operators in fuzzy logic

The choice of fuzzy implication as well as other connectives is an important problem in the theoretical development of fuzzy logic, and at the same time, it is significant for the performance of the systems in which fuzzy logic technique is employed. There are mainly two ways in fuzzy logic to define implication operators: (1) an implication operator is considered as the residuation of conjunction operator; and (2) it is directly defined in terms of negation, conjunction, and disjunction operators. The purpose of this paper is to determine the number of implication operators defined in the second way for some usual negation, conjunction and disjunction operators in fuzzy logic     

Compensatory neurofuzzy systems with fast learning algorithms

In this paper, a new adaptive fuzzy reasoning method using compensatory fuzzy operators is proposed to make a fuzzy logic system more adaptive and more effective. Such a compensatory fuzzy logic system is proved to be a universal approximator. The compensatory neural fuzzy networks built by both control-oriented fuzzy neurons and decision-oriented fuzzy neurons cannot only adaptively adjust fuzzy membership functions but also dynamically optimize the adaptive fuzzy reasoning by using a compensatory learning algorithm. The simulation results of a cart-pole balancing system and nonlinear system modeling have shown that: 1) the compensatory neurofuzzy system can effectively learn commonly used fuzzy IF-THEN rules from either well-defined initial data or ill-defined data; 2) the convergence speed of the compensatory learning algorithm is faster than that of the conventional backpropagation algorithm; and 3) the efficiency of the compensatory learning algorithm can be improved by choosing an appropriate compensatory degree.     

Entropy-based operations on fuzzy sets

By using a fuzzy entropy approach, three sets of new generalized operators are presented. After a general discussion on fuzzy entropy, the concept of an elementary entropy function of a fuzzy set is introduced. Using this mapping, the generalized intersections and unions are defined as mappings that assign the least and the most fuzzy membership grade to each of the elements of the domain of the operators, respectively. It is shown that these operators can be constructed from the conventional min and max operations. Next, two modified sets of operations are introduced. The second part of the paper investigates the applicability of the new operators in fuzzy logic controllers. Simulations have been carried out so as to determine the effects of the operators on the performance of the fuzzy controllers. It is concluded that the first set of operators does not provide stable control, but the performance of the fuzzy controller can be improved by using the modified operations for a class of plants     

Design of adaptive fuzzy logic controller based on linguistic-hedgeconcepts and genetic algorithms

In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and ran speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module, According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simple-shape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three well-known nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design.     

A novel approach to guarantee good robustness of fuzzy reasoning

The analysis of internal connective operators of fuzzy reasoning is very significant and the robustness of fuzzy reasoning has been calling for study. An interesting and important question is that, how to choose suitable internal connective operators to guarantee good robustness of rule-based fuzzy reasoning? This paper is intended to answer it. In this paper, Lipschitz aggregation property and copula characteristic of t-norms and implications are discussed. The robustness of rule-based fuzzy reasoning is investigated and the relationships among input perturbation, rule perturbation and output perturbation are presented. The suitable t-norm and implication can be chosen to satisfy the need of robustness of fuzzy reasoning. In 1-Lipschitz operators, if both t-norm and implication are copulas, the rule-based fuzzy reasoning is much more stable and more reliable. In copulas, if both t-norm and implication are 1-l_∞-Lipschitz, they can guarantee good robustness of fuzzy reasoning. The experiments not only illustrate the ideas proposed in the paper but also can be regarded as applications of soft computing. The approach in the paper also provides guidance for choosing suitable fuzzy connective operators and decision making application in rule-based fuzzy reasoning.     

广义区间二型模糊集合的词计算

普通的模糊集合是点值为二维的一型模糊集合,二型模糊集合(Type-2 fuzzy sets, T2 FS)是点值为三维的模糊集合, T2 FS比相应的一型难以理解和计算. 为了让人们更好地理解T2 FS并推广其应用, 本文提出了广义区间二型模糊集合(Generalized interval type-2 fuzzy sets, GIT2 FS)的定义, 并将其分成三类:离散型、半离散型及连续型,分别给出相应的数学表达式与扩展原理公式,并得到了GIT2 FS在两种不同的模糊逻辑算子下的词计算.