基于结构元理论的正规Type-2模糊集的质心

定义正规Type-2模糊集,并利用结构元理论给出正规Type-2模糊集的结构元表示和正规Type-2模糊集质心计算的确切步骤,得出正规Type-2模糊集质心的精确解.在正规Type-2模糊集的次隶属函数可以通过结构元线性表示时,给出其质心计算的近似结果.实例比较结果表明,所提出的方法计算简便,适用范围广,且在一定条件下近似解与精确解的拟合较好.     

区间Type-2 T-S间接自适应模糊控制

本文在Type-1 T-S间接自适应模糊控制器的基础上,利用Type-2模糊系统理论,提出了区间Type-2 T-S间接自适应模糊控制器的设计方法.由于该系统的规则前件是区间Type-2模糊集合,后件为精确数,使构造的控制方法既具备Type-2模糊集处理诸多不确定性的特点,能够减少由于规则不确定对系统的影响,同时又具有T-S模糊模型后件为各输入变量的线性组合的特点,可以提高系统的建模精度,减少系统的规则数等优点.本文利用Lyapunov合成方法,研究了在所有变量一致有界的意义下,闭环系统的全局稳定性,分析了区间Type-2 T-S间接自适应模糊控制系统的收敛性,并给出了系统参数的自适应律.通过倒立摆跟踪模型进行仿真,验证其有效性和优越性.     

Matlab与Visual C++混合编程的实现

从不同角度分析和研究了Visual C++与工程计算语言Matlab的接口技术.为工程设计人员提供了比较全面的Visual C++与Matlab的混合编程方法,并给出了各种方法的优缺点.通过二者的结合,既有效地利用了M atlab强大的数值计算能力和众多的函数,大大减少程序设计的工作量,又继承了Visual C++良好的程序界面,证明是一种很好的程序设计方法.     

实现Visual C++6.0与MATLAB的混合编程

本文从不同的角度论述了Visual C++6.0与工程计算语言MATLAB的混合编程。通过二者的结合,既有效地利用Matlab众多的函数,大大减少程序设计的工作量,又继承了VC良好的程序界面,因此不失为一种很好的程序设计方法。     

Matlab与C/C＋＋混合编程技术研究

Matlab具有强大的数值计算和分析等能力,而C/C++是目前最为流行的高级程序设计语言,两者互补结合的混合编程在科学研究和工程实践中具有非常重要的意义.因此,从Matlab调用C/C++代码及C/C++调用m文件两方面,深入地研究了它们之问混合编程的原理和实现机制,并且给出了具体条件下的混合编程方法和步骤.实例表明,提出的Matlab与C/C++混合编程方法是简洁、有效的.     

Matlab与C/C++混合编程接口及应用

Matlab具有很强的数值计算和分析等能力,而C/C++是目前最为流行的高级程序设计语言,两者互补结合的混合编程在科学研究和工程实践中具有非常重要的意义。从Matlab调用C/C++代码及C/C++调用m文件两方面,深入地研究了它们之间混合编程的原理和实现机制,并且给出了具体条件下的混合编程方法和步骤。实验表明,给出的Matlab与C/C++混合编程接口及应用方法是有效、实用的。     

一种C#与Matlab混合编程的快速实现方法

C#是一种最新的、面向对象和组建的高级编程语言。Matlab在科学与工程计算的多个领域都有着十分广泛的应用。因此如果把MATLAB强大的计算分析功能与高级语言的可视化能力相结合,将大大减少编程的工作量、保证程序的准确性,实现优势互补。快速实现从仿真到应用的转换该文介绍了一种C#与Matlab混合编程的快速实现方法.     

类型2模糊系统模型组合的噪声滤波器

通过对区间类型2模糊系统（interval Type-2 FLS）和高斯类型2模糊系统（Gaussian Type-2 FLS）进行组合,提出了适合图像脉冲噪声滤波的TFLS滤波器。用噪声密度不同的数字图像测试TFLS滤波器的性能,同时与区间类型2模糊系统FLS滤波器等进行对比。实验结果表明：TFLS滤波器在去除图像的脉冲噪声的同时,能较好地保留图像中的边缘细节信息,较显著改善图像视觉质量,客观评价指标PSNR值也有较明显提高。     

基于 Matcom 的 VC与 Matlab 混合编程的实现

Matlab 具有强大的数据处理和图形显示功能,但 Matlab 程序的执行效率非常低,而且其平台上开发的程序不能脱离本身的运行环境;VC++具有强大的用户界面开发功能,程序执行效率高,但程序编写繁琐,结合两者的优势可以快速开发出功能强大的应用系统.针对如何将两者结合这一问题,探讨了基于 Matcom4.5的 VC++与 Matlab 混合编程,并结合曲线拟合实例给出了具体的操作步骤.结论表明,利用此方法实现的 VC++与 Matlab 的混合编程操作简单,而且能够充分发挥两者的优势,缩短程序的开发周期.     

基于Windows消息的Matlab与C＋＋实时通讯方法

讨论了Matlab及C＋＋混合编程的优点,并根据当前情况下Matlab与C＋＋混合编程方法的不足,提出了一种使用Matlab下MEX文件通过标准Windows WM_COPYDATA消息进行Matlab与标准windows程序进行实时双向通讯的方法。详细分析了此种方法的难点,并给出了方法的具体实现。     

Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic

Uncertainty is an inherent part in control systems used in real world applications. The use of new methods for handling incomplete information is of fundamental importance. Type-1 fuzzy sets used in conventional fuzzy systems cannot fully handle the uncertainties present in control systems. Type-2 fuzzy sets that are used in type-2 fuzzy systems can handle such uncertainties in a better way because they provide us with more parameters and more design degrees of freedom. This paper deals with the design of control systems using type-2 fuzzy logic for minimizing the effects of uncertainty produced by the instrumentation elements, environmental noise, etc. The experimental results are divided in two classes, in the first class, simulations of a feedback control system for a non-linear plant using type-1 and type-2 fuzzy logic controllers are presented; a comparative analysis of the systems’ response in both cases was performed, with and without the presence of uncertainty. For the second class, a non-linear identification problem for time-series prediction is presented. Based on the experimental results the conclusion is that the best results are obtained using type-2 fuzzy systems.     

Polynomial regression interval-valued fuzzy systems

In recent years, the type-2 fuzzy sets theory has been used to model and minimize the effects of uncertainties in rule-base fuzzy logic system (FLS). In order to make the type-2 FLS reasonable and reliable, a new simple and novel statistical method to decide interval-valued fuzzy membership functions and probability type reduce reasoning method for the interval-valued FLS are developed. We have implemented the proposed non-linear (polynomial regression) statistical interval-valued type-2 FLS to perform smart washing machine control. The results show that our quadratic statistical method is more robust to design a reliable type-2 FLS and also can be extend to polynomial model.     

On the influence of using fuzzy extensions in linguistic fuzzy rule-based regression systems

Fuzzy Rule-Based Systems, FRBSs, are powerful tools to address regression problems. They can model the relationship between inputs and outputs by linguistic concepts. However, those FRBSs which are based on the conventional Type-1 fuzzy sets may not be able to handle some difficulties of real-world applications. In such situations, using novel representations of fuzzy sets seems like a good idea. Different extensions of fuzzy sets usually help to provide more precise models in the real-world problems. In this study, the influence of using fuzzy extensions in improving the efficiency of linguistic fuzzy rule-based regression models is investigated. For this purpose, a conventional Type-1 Mamdani FRBS is adapted to the three extensions of fuzzy sets, namely Interval Type-2, Intuitionistic, and Interval Type-2 Intuitionistic fuzzy sets. A two-pass method is proposed to define membership (non-membership) functions of these fuzzy sets; this method is based on the 3-tuples representation of the standard Type-1 membership functions. Wang and Mendel’s rule learning method is adapted to extract fuzzy rules from regression data. In order to tune the membership functions up to different extents, three evolutionary extensions are also presented for each type of the proposed FRBSs. Individual, internal, and external comparisons of the proposed FRBSs were done using 22 real-world regression datasets and statistical tests. Experimental results confirm that all the three proposed FRBSs outperform the classical Type-1 framework; furthermore, the Interval Type-2 Intuitionistic FRBS is the superior system so that an appropriate tuning of its parameters makes it the most accurate model.     

A fuzzy rule-based generation algorithm in interval type-2 fuzzy logic system for fault prediction in the early phase of software development

Reliability, a measure of software, deals in total number of faults count up to a certain period of time. The present study aims at estimating the total number of software faults during the early phase of software life cycle. Such estimation helps in producing more reliable software as there may be a scope to take necessary corrective actions for improving the reliability within optimum time and cost by the software developers. The proposed interval type-2 fuzzy logic-based model considers reliability-relevant software metric and earlier project data as model inputs. Type-2 fuzzy sets have been used to reduce uncertainties in the vague linguistic values of the software metrics. A rule formation algorithm has been developed to overcome inconsistency in the consequent parts of large number of rules. Twenty-six software project data help to validate the model, and a comparison has been provided to analyse the proposed model’s performance.     

二型直觉模糊集

二型模糊集和直觉模糊集都具有很强的实际应用背景.二型模糊集增强了系统处理不确定性的能力,直觉模糊集为解决人们判断问题所出现的犹豫信息提供了理论依据.本文在二型模糊集和直觉模糊集的基础上,给出了二型直觉模糊集的概念,证明了二型直觉模糊集是一型模糊集、直觉模糊集、区间值模糊集、区间值直觉模糊集的广义形式,讨论了二型直觉模糊集的基本运算和二型直觉模糊关系.最后,研究了基于二型直觉模糊理论的近似推理,并实例说明了二型直觉模糊集的实际应用背景.     

Applications of type-2 fuzzy logic systems to forecasting of time-series

In this paper, we begin with a type-1 fuzzy logic system (FLS), trained with noisy data. We then demonstrate how information about the noise in the training data can be incorporated into a type-2 FLS, which can be used to obtain bounds within which the true (noisefree) output is likely to lie. We do this with the example of a one-step predictor for the Mackey–Glass chaotic time-series [M.C. Mackey, L. Glass, Oscillation and chaos in physiological control systems, Science 197 (1977) 287–280]. We also demonstrate how a type-2 FLS can be used to obtain better predictions than those obtained with a type-1 FLS.     

基于TS模型的二型模糊控制器和观测器分析与设计

主要探讨一种新的系统方法——二型模糊系统在控制上的应用．文章先介绍了二型模糊集合和系统的基本概念和基本方法，然后集中推导TS模型下规则的输出形式，推广一型系统的特性，从而获得二型模糊控制器和观测器的表达式、稳定性以及其它特性分析方法，并以小车倒立摆仿真验证．最后文章从整体上分析和比较了传统系统、一型模糊系统和二型模糊系统的主要特点和不同点．     

二型模糊相似度及其应用

二型模糊集可以直接处理高度不确定性,并且具有很强的实际应用背景。基于二型模糊相似度的公理化定义,给出了新的二型模糊相似度计算公式。进一步,将二型模糊相似度与Yang-Shih方法相结合,用于二型模糊数据的聚类分析,聚类结果与Yang-Lin的结果进行了比较,实例表明新的相似度更合理。此外,基于二型模糊相似度,讨论了二型模糊信息系统的属性约简问题,给出了相应约简的分辨函数法,并通过实例说明了该方法的具体计算步骤。     

Designing an adaptive type-2 fuzzy logic system load frequency control for a nonlinear time-delay power system

In this paper, a combination of type-2 fuzzy logic system (T2FLS) and a conventional feedback controller (CFC) has been designed for the load frequency control (LFC) of a nonlinear time-delay power system. In this approach, the T2FLS controller which is designed to overcome the uncertainties and nonlinearites of the controlled system is in the feedforward path and the CFC which plays an important role in the transient state is in the feedback path. A Lyapunov–Krasovskii functional has been used to ensure the stability of the system and the parameter adjustment laws for the T2FLS controller are derived using this functional. In this training method, the effect of delay has been considered in tuning the T2FLS controller parameters and thus the performance of the system has been improved. The T2FLS controller is used due to its ability to effectively model uncertainties, which may exist in the rules and data measured by the sensors. To illustrate the effectiveness of the proposed method, a two-area nonlinear time-delay power system has been used and compared with the controller that uses the gradient-descend (GD) algorithm to tune the T2FLS controller parameters.