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 tak...
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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.
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 ...
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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.
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 c...
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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.
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 introduc...
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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.
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...
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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 knowledge-base.< >
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 previou...
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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.
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 un...
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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.
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 pr...
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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.
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 au...
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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.< >
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 k...
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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.
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