fuzzy

The classical fuzzy classifier consists of rules each one describing one of the classes. In this paper a new fuzzy model structure is proposed where each rule can represent more than one classes with different probabilities. The obtained classifier can be

PatternRecognitionLetters24(2003)

2195–2207

Supervisedfuzzyclusteringfortheidenti cation

offuzzyclassi ers

JanosAbonyi*,FerencSzeifert

DepartmentofProcessEngineering,UniversityofVeszprem,P.O.Box158,H-8201Veszprem,Hungary

Received9July2001;receivedinrevisedform26August2002

Abstract

268IEEE TRANSACTIONS ON FUZZY SYSTEMS,VOL.10,NO.2,APRIL2002

An Approach to Adaptive Control of Fuzzy Dynamic Systems

Gang Feng

Abstract—This paper discusses adaptive control for a class of fuzzy dynamic models.The adaptive control law is first designed in each local region and then constructed in global domain.It is shown that the resulting fuzzy adaptive control system is globally stable.Robustness issues of the adaptive control system are also addressed.A simulation example is given for demonstration of the application of th

In the present use of fuzzy logic systems tuning of their parameters has become an important issue. Many techniques, mainly based on the application of gradient descent, have been applied to this task generally in order to minimize a quadratic error functi

To be presented at ANNIE 2000: Smart Engineering System Design

St. Louis, MO, November 5-8, 2000 1

NON-DIFFERENTIABLE OPTIMIZATION OF

FUZZY LOGIC SYSTEMS

PAOLO DADONE HUGH F. VANLANDINGHAM

Virginia Tech, Department of Electrical and Computer Engineering

Blacksburg, Virginia – e-

Neurocomputing104(2013)10–25

ContentslistsavailableatSciVerseScienceDirect

Neurocomputing

journalhomepage:/locate/neucom

AHMM-basedadaptivefuzzyinferencesystemforstockmarketforecasting

Md.Ra ulHassana,n,KotagiriRamamohanaraob,JoarderKamruzzamanc,Musta zurRahmanb,M.MarufHossainb

a

DepartmentofInformationandComputerScience,KingFahdUniversityofPetroleumandMinerals,Dhahran31261,SaudiArabiaDepartmentofComputerScienceandSoftwareEngineering,TheUniversityofMelbourne,Victoria3010,Australiac

GippslandSchoolofIT,MonashUniversity,Churchill,VIC

We demonstrate that the UV/IR mixing problems found recently for a scalar $\phi^4$ theory on the fuzzy sphere are localized to tadpole diagrams and can be overcome by a suitable modification of the action. This modification is equivalent to normal ordering

We demonstrate that the UV/IR mixing problems found recently for a scalar $\phi^4$ theory on the fuzzy sphere are localized to tadpole diagrams and can be overcome by a suitable modification of the action. This modification is equivalent to normal ordering

We demonstrate th

Fuzzy Cognitive Maps FCMs is a new approach in modelling the behaviour and operation of complex systems. FCMs are proposed to be used in the modelling of control systems and particularly in the modelling of the upper part or supervisor of a hierarchical co

ComputersinIndustry39 1999.229–238

FuzzyCognitiveMaps:amodelforintelligentsupervisory

controlsystems

ChrysostomosD.Stylios),PeterP.Groumpos

Abstract

FuzzyCognitiveMaps FCMs.isanewapproachinmodellingthebehaviourandoperationofcomplexsystems.FCMsareproposedtobeusedinthemodellingof

We demonstrate that the UV/IR mixing problems found recently for a scalar $\phi^4$ theory on the fuzzy sphere are localized to tadpole diagrams and can be overcome by a suitable modification of the action. This modification is equivalent to normal ordering

We demonstrate that the UV/IR mixing problems found recently for a scalar $\phi^4$ theory on the fuzzy sphere are localized to tadpole diagrams and can be overcome by a suitable modification of the action. This modification is equivalent to normal ordering

We demonstrate th

Abstract. In this paper a fuzzy C-means (FCM) based approach for speech/non-speech discrimination is developed to build an effective voice activity detection (VAD) algorithm. The proposed VAD method is based on a soft-decision clustering approach built ove

Noise Subspace Fuzzy C-means Clustering for Robust Speech RecognitionJ.M. G´rriz1 , J. Ram´ 1 , J.C. Segura1 , o rez C.G. Puntonet2 , and J.J. Gonz´lez2 a1Dpt. Signal Theory, Networking and communications, University of Granada, Spain gorriz@ugr.es, WWW hom

This paper describes a teleoperation system in which an articulated robot performs a block pushing task based on hand gesture commands sent through the Intemet. A Fuzzy C-Means clustering method is used to classify hand postures as "gesture commands&q

5($/ 7,0( +$1' *(6785( 7(/(52%27,& 6<67(0 86,1* )8==< & 0($16 &/867(5,1* -8$1 :$&+6 85, .$57281 +(/0$1 67(51 <$(/ ('$1 'HSDUWPHQW RI ,QGXVWULDO (QJLQHHULQJ DQG 0DQDJHPHQW %HQ *XULRQ 8QLYHUVLW\ RI WKH 1HJHY %H¶HU 6KHHYD

第 34卷增刊 2

中南工业大学学报(自然科学版)J . C ENT . SO UT H UNI V. T ECH NOL.

2003年 7月

基于 BP神经网络和「ZZ集的模式 U Y识别算法及其 MatLab实现蒋良孝，蔡之华.刘钊

(中国地质大学计算机科学与技术系，湖北武汉 430074)摘要:提出了一种基于BP神经网络和 Fuzzy集的模式识别算法， MatLab系统环境下实现了并在这种算法.实践证明，运用该算法进行模式识别的准确率非常高， MatLab编程简捷明了，使用具有很强的实用性和较大的应用前景.关键词:模式识别; BP神经网络; Fuzzy集; MatLab中图分类号;TP301. 6

模式即对某一事物或其他一些感兴趣项目的定量或结构上的描述，它可以用一个标准激励或用取自标准激励和它们相互关系的属性组成的矢量来表示，一组具有公共特性的模式可以看作一个模式类.通过机器进行模式识别的主要问题就是如何采用更好的计算机处理技术自动地、人尽可能少介人把模式分到各自的类中.更广泛地说，模式识别就是将测量结果、激励或输人模式分配到有意义的类别中.目前，可以用于模式识别的算法很多，但大多数算法都存在分类准确率低的缺陷.为了提高模式识别的准确率，本文作