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THE MAX-MIN DELPHI METHOD AND FUZZY DELPHI METHOD VIA FUZZY INTEGRATION

FUZZY SETS AND SYSTEMS 1993年第3期55卷 241-253页

作者： ISHIKAWA, A AMAGASA, M SHIGA, T TOMIZAWA, G TATSUTA, R MIENO, H UNIV TEXAS AUSTINTX 78712 DAITOU BUNKA UNIV TOKYOJAPAN HAKUHOUDOU INC TOKYOJAPAN SCI UNIV TOKYO TOKYO 162JAPAN DOKKYO UNIV SOKASAITAMAJAPAN

The traditional Delphi method is one of the effective methods which enables forecasting by converging a possibility value through the feedback mechanism of the results of questionnaires, based on experts' judgments. Some points needing revision are: (1) By pinpointing the intuition of the first response on the part of experts, feasible inference values need to be extracted so that the quality-oriented and semantic structure of the responses may be analyzed. (2) By removing the effect caused by feedback in the Delphi method, natural and non-converged results need to be acquired;Moreover, two and more repetitive surveys are likely to cause a decline in the response rate, which may produce negative effects in the ensuing analyses. (3) In general, as it is repeated, the survey becomes more costly and time-consuming. In order to resolve these issues, we have identified two kinds of membership functions in regard to 'the attainable period with a high degree' and 'the unattainable period with a high degree'. Next, through the implementation of the Max-Min Fuzzy Delphi Method and the New Delphi Method via Fuzzy Integration, we have developed algorithms which enable forecasting attainable periods. Third, we have applied such algorithms to two concrete questions, compared the result with one obtained from the Delphi method, and ascertained the feasible outcome. While more examination needs to be undertaken, the new methods look valid and applicable to further analyses of other questions and items on questionnaires. While both methods can forecast attainable periods, using these methods simultaneously as well as the traditional Delphi method, may prove a really effective result.

关键词： FUZZY DELPHI METHOD FUZZY INTEGRATION membership functionS TECHNOLOGICAL FORECASTING EXTENT OF EXPERTISE MAX-MIN NORMATIVISM triangular membership function

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A fuzzy approach to identify fish red spot disease

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GREY SYSTEMS-THEORY AND APPLICATION 2020年第3期10卷 249-263页

作者： Bhoi, Sourav Kumar Panda, Sanjaya Kumar Jena, Kalyan Kumar Mallick, Chittaranjan Khan, Akhtar Biju Patnaik Univ Technol Parala Maharaja Engn Coll Dept CSE Berhampur India Natl Inst Technol Warangal Dept CSE Warangal Andhra Pradesh India Biju Patnaik Univ Technol Parala Maharaja Engn Coll Dept Math Berhampur India Indian Inst Informat Technol Design & Mfg Kurnool Kurnool India

Purpose Fish are considered as one of the important aquatic animals in the planet. They play a vital role in the nutrient cycle. They can be considered as one of the healthy food for human beings. They can also act as a solution for some of the human health problems. If fish are affected by several diseases, they in turn provide an adverse effect on human health. Therefore, it is very much essential to protect fish from being affected by any diseases. Design/methodology/approach This paper is mainly focused on the identification of the red spot diseased area in fish. In this work, a fuzzy rule based method (FRBAM) and triangular membership function (TMFN) is used to identify the red spot disease (RSD) in the fish by analyzing several red spot diseased fish (RSDF) images. The canny edge detector is used for intermediate processing of RSDF images. Findings The proposed method is able to identify the red pixels over the fish by marking the affected area with red color by using a standard RGB model. Originality/value The proposed method follows FRBAM and TMFN in order to detect the RSD and canny edge detector for processing of RSDF images. Finally, it is tested using ten different image sizes and the results show its better performance in terms of detection of RSD affected regions of fish and execution time.

关键词： Fuzzy rule based method Red green blue model Red spot diseased fish triangular membership function

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Comparison between Type-1 Fuzzy membership functions for Sign Language Applications

Comparison between Type-1 Fuzzy Membership Functions for Sig...

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International Conference on Microelectronics, Computing and Communications (MicroCom)

作者： Saha, Sriparna Bhattacharya, Saurav Konar, Amit Jadavpur Univ Elect & Telecommun Engn Dept Kolkata India

ISBN: (纸本)9781467366212

The paper presents a comparison between different membership functions based type-1 fuzzy set for automatic hand gesture recognition for American Sign Language recognition. First pre-processing of the images is done using skin color based segmentation, morphological operations and to extract the hand gesture image from the background, Sobel edge detection technique is performed. Then the image is sub-divided into 9 quadrants and for each quadrant, the area enclosed by the image in that specific quadrant is calculated. Based on areas from 15 images for a particular quadrant, the Gaussian three membership curves triangular, trapezoidal and Gaussian are designed. Now for an unknown gesture, nine membership values are determined and the summation of these membership values produces the strength of the unknown hand gesture matched with a known gesture. The highest strength obtained for a known gesture is the desired result. Experimentally, it is found that trapezoidal membership function outperforms the other two membership functions and gives overall an accuracy of 85.83%.

关键词： Gaussian membership function triangular membership function trapizoidal membership function type-1 fuzzy set sign language probability

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A New Paradigm For Generation Of Fuzzy membership function 8

A New Paradigm For Generation Of Fuzzy Membership Function

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IEEE 8th International Advance Computing Conference (IACC)

作者： Vaidya, Anagha V. Metkewar, Pravin S. Naik, Sachin A. Symbiosis Int SICSR Pune 411016 Maharashtra India

ISBN: (纸本)9781538666784

A membership function (MF) is a curve that defines how each point in the input space is mapped to a membership value (or degree of membership) between 0 and 1. The input space is sometimes referred to as the universe of discourse. This paper further develops the fuzzy-based algorithm to add the feature of automatic membership function generation in the fuzzy logic module of the algorithm. From this context, a short review of related work in membership function generation is given, and rules associated with it have been incorporated. In this paper, a one step ahead to the nature of the fuzzy logic-based design, a fitness finding method has been proposed. This paper also evaluates the proposed algorithm for deriving membership function based on association rule using control parameters with its implementation. The algorithm is applied by considering a case study of share market data and results are analyzed and compared with the intuitive cases

关键词： Association rules Decision tree Fuzzy Logic Intuitive cases triangular membership function

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Fuzzy K-means clustering models for triangular fuzzy time trajectories

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Statistical Methods and Applications 2002年第1期11卷 21-40页

作者： Coppi, Renato Dipartimento di Statistica Probabilità e Statistiche Applicate Università degli Studi di Roma La Sapienza 00185 Roma Piazzale Aldo Moro 5 Italy

We focus our attention on the classification of fuzzy time trajectories with triangular membership function, described by a given set of individuals. To this purpose, we adopt a fully informational approach, explicitly recognizing the informational nature shared by the ingredients of the classification procedure: the observed data (Empirical Information) and the classification model (Theoretical Information). In particular, by supposing that the informational paradigm has a fuzzy nature, we suggest three fuzzy clustering models allowing the classification of the triangular fuzzy time trajectories, based on the analysis of the cross sectional and/or longitudinal characteristics of their components (centers and spreads). Two applicative examples are illustrated. © Springer-Verlag 2002.

关键词： Cross sectional and/or longitudinal double fuzzy clustering Fuzzy time array Fuzzy time trajectory Informational support triangular membership function

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2010 IEEE World Congress on Computational Intelligence

作者： Manna, Sukanya Mendis, B. Sumudu U. Australian Natl Univ Sch Comp Sci Informat & Human Centered Comp Grp Canberra ACT 0200 Australia

ISBN: (纸本)9781424469208

In this paper we present a hybrid measure of semantic word similarity using fuzzy inference system which combines both the corpus based distance measures as well as gloss overlap to get the final similarity between two words. We use WordNet as a lexical dictionary to get semantic information about words. We show that this new measure reasonably correlates to human judgments and the average performance is boosted by using triangular membership function in the output.

关键词： WordNet computational linguistics corpus based distance measure fuzzy inference system fuzzy reasoning fuzzy set theory fuzzy systems fuzzy word similarity inference mechanisms lexical dictionary semantic approach text analysis triangular membership function

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Attribute Reduction Algorithm of Continuous Domain Decision Table Based on Similar Matrix

Attribute Reduction Algorithm of Continuous Domain Decision ...

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5th International Symposium on Computational Intelligence and Design (ISCID)

作者： Li Renguo China West Normal Univ Coll Mathemat & Infomat Nanchong Sichuan Peoples R China

ISBN: (纸本)9780769548111;9781467326469

Combining fuzzy set with rough set, attribute reduction algorithm of continuous domain decision table is studied. First, continuous attribute value are transformed into fuzzy value with triangular membership function. And then, similarity degree of two fuzzy objects and similarity class of each fuzzy object are defined, in the meantime, characteristic vector of continuous attribute which is made up of similarity class of each fuzzy object is provided. Next, digital characteristic vector of continuous attribute is presented and similar matrix of continuous attributes is proposed. Finally, a new attribute reduction algorithm is provided. Also, the new algorithm is verified through an illustrative example.

关键词： Fuzzy Set Rough Set triangular membership function Similar Matrix Attribute Reduction

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Plasmodium vivax in Thin Blood Smear Classification Using Fuzzy Inference System With Automatic membership functions and Rules Generation 21

Plasmodium vivax in Thin Blood Smear Classification Using Fu...

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21st International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

作者： Turakit, Satanan Auephanwiriyakul, Sansanee Theera-Umpon, Nipon Chiang Mai Univ Fac Engn Comp Engn Dept Grad Sch Chiang Mai Thailand Chiang Mai Univ Fac Engn Biomed Engn Inst Comp Engn DeptBiomed Engn & Innovat Res Ctr Chiang Mai Thailand Chiang Mai Univ Fac Engn Biomed Engn Inst Elect Engn DeptBiomed Engn & Innovat Res Ctr Chiang Mai Thailand

ISBN: (纸本)9798350381566;9798350381559

Malaria can be spread to human by anopheles female mosquitoes. There are four prevalent Plasmodium species responsible for malaria in humans. One of the four species, Plasmodium vivax or P. vivax, is selected in this paper. Its infected cell detection system based on image processing is developed. We automatically generate membership functions of inputs using the possibilistic fuzzy C-means. The Wang-Mendel (WM) method is used to automatically generate rules in the Mamdani fuzzy inference system. Although there are misclassifications because of the wrongly classified cells with similar characteristics to the opposite class, the correct classification rate of the training data is 93.65%. Whereas that of the blind test data set is 99.58%.

关键词： triangular membership function possibilistic fuzzy C-Means Wang-Mendel Method Type-1 fuzzy Plasmodium Vivax

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Efficiently Exploring Clusters using Genetic Algorithm and Fuzzy Rules 4

Efficiently Exploring Clusters using Genetic Algorithm and F...

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4th International Conference on Computer Communication and Informatics (ICCCI)

作者： Pitambare, Dinesh P. Kamde, Pravin M. Sinhgad Coll Engn Pune 41 Maharashtra India Sinhgad Coll Engn Dept Comp Engn Pune 41 Maharashtra India

ISBN: (纸本)9781479923526

Cluster is bunch of similar items. Unsupervised classification of patterns into clusters is known as clustering. It is useful in knowledge discovery in data. Clustering is able to deal with different data types. Fuzzy rules are used for data intelligence illustration purpose. User gets highly interpretable discovered clusters using fuzzy rules. To generate accurate fuzzy rules triangular membership function is used. This paper is proposed to automatically explore the number of clusters efficiently from a given numeric dataset. To discover clusters efficiently genetic algorithm is used. Fuzzy rules are generated from genetic algorithm, whose derivative is best fuzzy rules. Best rules are obtained among generated fuzzy rules according to maximum fitness value. Proposed work is carried out on benchmark numeric datasets to validate the capability of the proposed system.

关键词： Clustering best rule fuzzy clustering fuzzy rule fuzzy set theory triangular membership function genetic algorithm

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A Self-tuning of membership functions for Medical Diagnosis

A Self-tuning of Membership Functions for Medical Diagnosis

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11th International Conference on Enterprise Information Systems

作者： Soonthornphisaj, Nuanwan Teawtechadecha, Pattarawadee Kasetsart Univ Dept Comp Sci Fac Sci Bangkok Thailand

ISBN: (纸本)9783642013461

In this paper, a self-tuning of membership functions for fuzzy logic is proposed for medical diagnosis. Our algorithm uses decision tree as a tool to generate three kinds of membership functions which are triangular, bell shape and Gaussian Curve. The system can automatically select the best form of membership function for the classification process that call provide the best classification result. The advantage of our system is that it doesn't need the expert to create membership functions for each feature. But the system call create various membership functions Using learning algorithm that learns from the training set. In some domains, user can provide prior knowledge that call be used to enhance the performance of the classifier. However, in medical domain, we found that some diseases are difficult to diagnose. It Would not be a problem if that disease has been completely explored in medical area. In order to rule Out the patient, we need a domain expert to provide the membership functions for many attributes obtained from the laboratory test. Since the disease has not been completely explored in medical area, the membership function provided by the expert might be biased and lead to the poor classification performance. The performance Of Our proposed algorithm has been investigated oil 2 medical data sets. The experimental results show that our approach can effectively enhance the classification performance compare to neural networks and the traditional fuzzy logic.

关键词： Fuzzy logic Gaussian curve triangular membership function Bell Shape membership function Medical diagnosis

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