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检索条件"主题词=Inference Algorithms"

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Fuzzy inference based subjective clustering method

Fuzzy inference based subjective clustering method

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： T. Miyazaki M. Hagiwara Keio University Yokohama Japan

In this paper a new subjective clustering method using fuzzy inference is proposed. Changing some parameters interactively, a user can reflect his/her knowledge or intuition for the clustering. The proposed method takes into account of both: (1) connectivity of data, and (2) linearity of the data distribution. In addition, it represents shapes of clusters by membership functions and uses fuzzy reasoning to reflect the subjectivity of a user effectively. The proposed method is also effective not only for clustering but also for other applications such as data analysis, assumption test, modeling, concept formation support systems, etc. The validity of the proposed method is confirmed by computer simulation.

关键词： Clustering methods Shape Humans Computer simulation Linearity Clustering algorithms inference algorithms System testing Artificial neural networks Fuzzy systems

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Feature-based target recognition with Bayesian inference

Feature-based target recognition with Bayesian inference

引用

Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： Jun Liu Kuo-Chu Chang Center of E-cellence in Command Control Communications and Intelligence School of Information Technology and Engineering George Mason University Fairfax VA USA

ISBN: (纸本)0818671262

The problem of target classification with high-resolution fully polarimetric, synthetic aperture radar (SAR) imagery is considered. The paper summarizes our recent work in SAR target recognition using a feature-based Bayesian inference approach. The approach works on the selected features. Features are chosen such that the separabilities of the original data are well maintained for later classification. Once the original data is mapped into feature space, the conditional probability distributions of features given the target are estimated statistically, which are then used to calculate the probabilities that a target belongs to one of the given classes based on the observed features. The target is assigned to the class with the highest probability. A comparison between the above technique and the traditional statistical approaches such as nearest mean and Fisher pairwise is illustrated based upon performance on a fully polarimetric ISAR (inverse SAR) image data set.

关键词： Bayesian methods Azimuth Probability distribution Vehicles Computer networks Radar imaging Classification algorithms inference algorithms Laboratories Millimeter wave radar

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A fuzzy inference network for classification

A fuzzy inference network for classification

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Y. Cai H.K. Kwan Department of Electrical Engineering University of Windsor Windsor ONT Canada

A fuzzy inference network (FIN) is proposed. The proposed FIN preserves the advantages of both fuzzy classification algorithm and neural networks. It can learn membership functions directly from training samples and classify patterns according to the membership values. As efficient self-organizing learning algorithm is also presented.

关键词： Fuzzy neural networks inference algorithms Classification algorithms Backpropagation algorithms Neurons Neural networks Training data

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Inductive inference of logical DES controllers using the L* algorithm

Inductive inference of logical DES controllers using the L* ...

引用

American Control Conference (ACC)

作者： Xiaojun Yang M. Lemmon P. Antsaklis Department of Electrical Engineering University of Notre Dame Notre Dame IN USA

It is well known that learning procedures such as the L* algorithm will infer minimal deterministic finite automatons (DFA) in polynomial time through the use of membership and equivalence queries. This paper introduces a modification of the L*-algorithm that can be used for the inductively inferring optimal logical DES controllers in which prior knowledge of the plant is confined to a finite lookahead window of predicted behaviours.

关键词： inference algorithms Marine vehicles Automatic control Doped fiber amplifiers Polynomials Control system synthesis Uncertainty Learning automata Optimal control Discrete event systems

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On the optimality of the Takagi-Sugeno-Kang fuzzy inference mechanism

On the optimality of the Takagi-Sugeno-Kang fuzzy inference ...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： T.A. Johansen Department of Engineering Cybernetics Norwegian Institute of Technology University of Trondheim Trondheim Norway

It is shown that the Takagi-Sugeno-Kang (TSK) fuzzy inference mechanism is optimal with respect to a certain criterion that quantifies the controversy between the inference made, and the knowledge represented in the k... 详细信息

关键词： Takagi-Sugeno-Kang model inference mechanisms Fuzzy logic Fuzzy sets Fuzzy systems inference algorithms Fuzzy control Solid modeling Cybernetics Electrical equipment industry

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An advanced updating increment to speed-up the measurement of AC skin impedance based on a fuzzy inference approach

An advanced updating increment to speed-up the measurement o...

引用

International IEEE/IAS Conference on Industrial Automation and Control: Emerging Technologies

作者： B.R. Chang Department of Electronic Engineering Kao Yuan Junior College of Technology and Commerce Kaohsiung Taiwan

Conventional methods for determining AC skin impedance with a wide range of frequency process DC to 1 MHz resulting from the off-line estimation are accurate but lack real-time convenience. In the author's previous proposed methods, the direct measurement is faster than conventional methods because it uses a real-time, on-line measurement and computation, and requires only one high frequency input signal. However, the algorithm to specify the suitable updating value (the defuzzification output multiplied by a gain) in fuzzy inference system is not unique, and the selecting an appropriate updating method for the estimation process of AC skin impedance in fuzzy inference system is necessitated. In order to speed-up measurement, an advanced updating gain, which has a significant effect on the time-performance, is introduced in the real-time analysis of AC skin impedance based on a fuzzy inference system.

关键词： Impedance measurement Velocity measurement Skin Fuzzy systems Frequency measurement Frequency estimation inference algorithms Image analysis Equivalent circuits Electric resistance

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TransFuzzien-a transputer-based fuzzy logic inference engine

TransFuzzien-a transputer-based fuzzy logic inference engine

引用

Australian and New Zealand Conference on Intelligent Information Systems

作者： R.S. Bowyer Salisbury Electronic Warfare Division Defence Science and Technology Organisation Salisbury SA Australia

The paper introduces a software package called TransFuzzien, that has been developed to perform rule based fuzzy logic inferencing. TransFuzzien comprises two parts. The first part is written in C/sup ++/, and runs under Microsoft Windows on a PC, and the second part, which is written in Occam 2, runs on a transputer target system. The graphical user interface is described, together with various features of the system. The inference engine for the system uses fuzzy logic principles, and a parallel processing algorithm for inferencing is developed. The processing is performed on the Inmos Transputer, and the inferencing algorithm is realised in the Occam 2 programming language. A feature of the system is the ability to select various inferencing methods via the user interface.

关键词： Fuzzy logic Engines Fuzzy sets Graphical user interfaces Artificial intelligence Hardware Fuzzy systems inference algorithms User interfaces Expert systems

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Comparison of different fuzzy algorithms in a function behavior recognition problem

Comparison of different fuzzy algorithms in a function behav...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： E. Druon E. Duflos V. Boulet D. Willaeys P. Vanheeghe Department Signaux et Syst&mes I.S.E.N Lille France Laboratoire d' Automatique Industrielle et Humaine Universié de Valenciennes France

Fuzzy logic is now used in a large number of application domains including control and estimation. The aim of this paper is to show how techniques based on fuzzy logic can be used in a function behavior recognition problem when data are corrupted by noise. Different algorithms based on fuzzy logic controllers are studied. Their speed and precision performance is analyzed.

关键词： Fuzzy logic Approximation algorithms inference algorithms Fuzzy control Performance analysis Additive noise Gaussian noise Algorithm design and analysis Equations

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Fuzzy model of joystick operation for cursor positioning on a CRT on cervical injuries

Fuzzy model of joystick operation for cursor positioning on ...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： M. Ide K. Kurosu Spinal Injuries Center Fukuoka Japan Kinki University Fukuoka Japan

This report deal with operational characteristics of a pointing device for persons who have cervical injuries and who suffer from spasms and paralysis of extremities. The characteristics are modeled by algorithm of automatic generation for fuzzy inference rule of simplified method. The teaching data of fuzzy inference are obtained on cursor positioning experiments of a joystick with single-speed floating action. It is observed that the fuzzy models exchange the operation rules near by target, and that cervical injury's exchange distances are farther than abled person's exchange distances.< >

关键词： Cathode ray tubes Injuries inference algorithms Character generation Extremities Mice Education Algorithm design and analysis Fingers

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Intelligent control using a neuro-fuzzy network

Intelligent control using a neuro-fuzzy network

引用

Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots

作者： M. Iskarous K. Kawamura Center for Intelligent Systems Vanderbilt University Nashville TN USA

Intelligent control techniques have emerged to overcome some deficiencies in conventional control methods in dealing with complex real-world systems. These problems include knowledge adaptation, learning, and expert knowledge incorporation. In this paper, a hybrid network that combines fuzzy inferencing and neural networks is used to model and to control complex dynamic systems. The network takes advantage of the learning algorithms developed for neural networks to generate the knowledge base used in fuzzy inferencing. The network as used to model and to control a robot arm with flexible pneumatic actuator. Comparison with a nonlinear control technique used for the robot joints is also presented.

关键词： Intelligent control Fuzzy neural networks Neural networks Control systems Fuzzy control Fuzzy systems Control system synthesis inference algorithms Robot control Pneumatic actuators

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