• Title/Summary/Keyword: fuzzy classification

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A Design of Control Chart for Fraction Nonconforming Using Fuzzy Data (퍼지 데이터를 이용한 불량률(p) 관리도의 설계)

  • 김계완;서현수;윤덕균
    • Journal of Korean Society for Quality Management
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    • v.32 no.2
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    • pp.191-200
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    • 2004
  • Using the p chart is not adequate in case that there are lots of data and it is difficult to divide into products conforming or nonconforming because of obscurity of binary classification. So we need to design a new control chart which represents obscure situation efficiently. This study deals with the method to performing arithmetic operation representing fuzzy data into fuzzy set by applying fuzzy set theory and designs a new control chart taking account of a concept of classification on the term set and membership function associated with term set.

Adaptive Transform Image Coding by Fuzzy Subimage Classification

  • Kong, Seong-Gon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.2 no.2
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    • pp.42-60
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    • 1992
  • An adaptive fuzzy system can efficiently classify subimages into four categories according to image activity level for image data compression. The system estimates fuzzy rules by clustering input-output data generated from a given adaptive transform image coding process. The system encodes different images without modification and reduces side information when encoding multiple images. In the second part, a fuzzy system estimates optimal bit maps for the four subimage classes in noisy channels assuming a Gauss-Markov image model. The fuzzy systems respectively estimate the sampled subimage classification and the bit-allocation processes without a mathematical model of how outputs depend on inputs and without rules articulated by experts.

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The Design of GA-based TSK Fuzzy Classifier and Its application (GA기반 TSK 퍼지 분류기의 설계 및 응용)

  • 곽근창;김승석;유정웅;전명근
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2001.12a
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    • pp.233-236
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    • 2001
  • In this paper, we propose a TSK-type fuzzy classifier using PCA(Principal Component Analysis), FCM(Fuzzy C-Means) clustering and hybrid GA(genetic algorithm). First, input data is transformed to reduce correlation among the data components by PCA. FCM clustering is applied to obtain a initial TSK-type fuzzy classifier. Parameter identification is performed by AGA(Adaptive Genetic Algorithm) and RLSE(Recursive Least Square Estimate). we applied the proposed method to Iris data classification problems and obtained a better performance than previous works.

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Classification of remotely sensed images using fuzzy neural network (퍼지 신경회로망을 이용한 원격감지 영상의 분류)

  • 이준재;황석윤;김효성;이재욱;서용수
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.35S no.3
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    • pp.150-158
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    • 1998
  • This paper describes the classification of remotely sensed image data using fuzzy neural network, whose algorithm was obtained by replacing real numbers used for inputs and outputs in the standard back propagation algorithm with fuzzy numbers. In the proposed method, fuzzy patterns, generated based on the histogram ofeach category for the training data, are put into the fuzzy neural network with real numbers. The results show that the generalization and appoximation are better than that ofthe conventional network in determining the complex boundary of patterns.

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A Study on a Method of Pattern Classification by Fuzzy Algorithm (Fuzzy 연산 식을 이용한 형상식별 방법에 관한 연구)

  • 김장복;김순협
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.5 no.1
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    • pp.49-53
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    • 1980
  • Since Zadeh had published the fuzzy set theory at 1965, it has been applied to many fields such as realizability of communication nets, automatic control, learning systems, switching circuits. In this paper, the method of applying a fuzzy logic to a pattern classification is studied and the difference of fuzzy logic from Boolean algebra is discussed. Classfication experiment is carried out 16 persons' photos of three families by fourty male and female observers and recognition rate 94% is obtained.

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Fuzzy Control of Anti -Sway Motion for a Remote Crane Operation

  • Park, Sun-Won;Kang, E-Sok
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.42.1-42
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    • 2001
  • This paper presents a fuzzy-based method for classification skin color object in a complex background under varying illumination. Parameters of fuzzy rule base are generated using a genetic algorithm(GA). The color model is used in the YCbCr color space. We propose a unique fuzzy system in order to accommodate varying background color and illumination condition. This fuzzy system approach to skin color classification is discussed along with an overview of YCbCr color space.

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Stability of the classifier based on fuzzy similarity in generalized Lukasiewicz Structure

  • Sampo, J.;Luukka, P.
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.1324-1329
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    • 2004
  • In this article we have tested stability of classifier based on fuzzy similarity in generalized Lukasiewicz structure. Two different tests for stability was made:In on test stability was checked respect to weight parameters and other test was carried out for idealvectors. Tests have made with three different classification problems.

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Fuzzy-based Threshold Controlling Method for ART1 Clustering in GPCR Classification (GPCR 분류에서 ART1 군집화를 위한 퍼지기반 임계값 제어 기법)

  • Cho, Kyu-Cheol;Ma, Yong-Beom;Lee, Jong-Sik
    • Journal of the Korea Society of Computer and Information
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    • v.12 no.6
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    • pp.167-175
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    • 2007
  • Fuzzy logic is used to represent qualitative knowledge and provides interpretability to a controlling system model in bioinformatics. This paper focuses on a bioinformatics data classification which is an important bioinformatics application. This paper reviews the two traditional controlling system models The sequence-based threshold controller have problems of optimal range decision for threshold readjustment and long processing time for optimal threshold induction. And the binary-based threshold controller does not guarantee for early system stability in the GPCR data classification for optimal threshold induction. To solve these problems, we proposes a fuzzy-based threshold controller for ART1 clustering in GPCR classification. We implement the proposed method and measure processing time by changing an induction recognition success rate and a classification threshold value. And, we compares the proposed method with the sequence-based threshold controller and the binary-based threshold controller The fuzzy-based threshold controller continuously readjusts threshold values with membership function of the previous recognition success rate. The fuzzy-based threshold controller keeps system stability and improves classification system efficiency in GPCR classification.

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Unsupervised Image Classification through Multisensor Fusion using Fuzzy Class Vector (퍼지 클래스 벡터를 이용하는 다중센서 융합에 의한 무감독 영상분류)

  • 이상훈
    • Korean Journal of Remote Sensing
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    • v.19 no.4
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    • pp.329-339
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    • 2003
  • In this study, an approach of image fusion in decision level has been proposed for unsupervised image classification using the images acquired from multiple sensors with different characteristics. The proposed method applies separately for each sensor the unsupervised image classification scheme based on spatial region growing segmentation, which makes use of hierarchical clustering, and computes iteratively the maximum likelihood estimates of fuzzy class vectors for the segmented regions by EM(expected maximization) algorithm. The fuzzy class vector is considered as an indicator vector whose elements represent the probabilities that the region belongs to the classes existed. Then, it combines the classification results of each sensor using the fuzzy class vectors. This approach does not require such a high precision in spatial coregistration between the images of different sensors as the image fusion scheme of pixel level does. In this study, the proposed method has been applied to multispectral SPOT and AIRSAR data observed over north-eastern area of Jeollabuk-do, and the experimental results show that it provides more correct information for the classification than the scheme using an augmented vector technique, which is the most conventional approach of image fusion in pixel level.

Classification of Multi-sensor Remote Sensing Images Using Fuzzy Logic Fusion and Iterative Relaxation Labeling (퍼지 논리 융합과 반복적 Relaxation Labeling을 이용한 다중 센서 원격탐사 화상 분류)

  • Park No-Wook;Chi Kwang-Hoon;Kwon Byung-Doo
    • Korean Journal of Remote Sensing
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    • v.20 no.4
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    • pp.275-288
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    • 2004
  • This paper presents a fuzzy relaxation labeling approach incorporated to the fuzzy logic fusion scheme for the classification of multi-sensor remote sensing images. The fuzzy logic fusion and iterative relaxation labeling techniques are adopted to effectively integrate multi-sensor remote sensing images and to incorporate spatial neighboring information into spectral information for contextual classification, respectively. Especially, the iterative relaxation labeling approach can provide additional information that depicts spatial distributions of pixels updated by spatial information. Experimental results for supervised land-cover classification using optical and multi-frequency/polarization images indicate that the use of multi-sensor images and spatial information can improve the classification accuracy.