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SENSITIVITY ANALYSIS OF HIERARCHICAL HYBRID FUZZY - NEURAL NETWORK

INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS 2015年第3期8卷 1837-1854页

作者： Xing Haihua Yu Xianchuan Hu Dan Dai Sha Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China Hainan Normal Univ Coll Informat Sci & Technol Haikou Peoples R China

To identify the important attributes of complex system, which is high-dimensional and contain both discrete and continuous variables, this paper proposes a sensitivity analysis method of hierarchical hybrid fuzzy - neural network. We derive the sensitivity indexes of discrete and continuous variables through the differential method. To verify the effectiveness of our method, this study employed a man-made example and a remote sensing image classification example to test the performance of our method. The results show that our method can really identify the important variables of complex system and discover the relations between input and output variables;therefore, they can be applied to simplify the model and improve the classification accuracy of model.

关键词： Hierarchical hybrid fuzzy - neural network Sensitivity analysis Differential method Takagi-Sugeno model triangular membership function

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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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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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A novel hybrid method for better evaluation: Evaluating university instructors teaching performance by combining conventional content analysis with fuzzy rule based systems

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第10期38卷 12565-12568页

作者： Basaran, Murat Alper Kalayci, Nurdan Atay, Mehmet Tarik Nigde Univ Fac Sci & Letters Dept Math TR-51240 Nigde Turkey Gazi Univ Gazi Fac Educ Dept Educ Sci TR-06500 Ankara Turkey

Even though engineering and applied sciences deal with numerical data, they have successfully implemented fuzzy logic by using fuzzy rule based (FRB) systems by verbalizing data. On the other hand, social sciences such as sociology, education and physiology transform verbal type data into numerical values by using Likert type scale. Despite the fact that fuzzy set theory deals with verbal data very powerfully, social science fields in general have avoided to implement it to their verbal data up until now. One of the most active research areas in education field which generates verbal data is student evaluation of teaching (SET) questionnaires which are related to Total Quality Management applications in most of the competitive universities in the world. In this paper, we propose a novel hybrid method, which combines conventional content analysis (CCA) method and FRB systems and this new hybrid method is more suitable for the verbal data obtained from SET questionnaires. This novel CCA-FRB (conventional content analysis based fuzzy rule based systems) method uses a sample of 138 junior students from Gazi University in Turkey to implement the proposed method. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Fuzzy logic Fuzzy set triangular membership function Student evaluation of teaching Conventional content analysis TQM

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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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A Fuzzy Approach to Weighted Temporal Mining Using Automatic Weight Assignment With Reduced Complexities

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JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY 2010年第4期4卷 425-441页

作者： Balasubramanian, C. Duraiswamy, K. Palanisamy, V. KS Rangasamy Coll Technol Dept CSE Tiruchengode Tamil Nadu India Info Inst Engn Coimbatore Tamil Nadu India

Databases and data warehouses have become a vital part of many organizations. So useful information and helpful knowledge have to be mined from transactions. In real life, media information has time attributes either implicitly or explicitly called as temporal data. The temporal database consists of items which are prioritized by assigning weights. These weights are assigned automatically using the scheduling concept aging algorithm in such a way that starvation is avoided. This paper focuses on an encoding method for the temporal database that reduces the memory utilization during processing. The fuzzy approach is applied by assigning the triangular membership functions to the weighted items which gives better results than quantitative values. Weighted association rule mining is performed using the weighted support and confidence measures which are applied to the AprioriTid algorithm as a new approach to the already existing algorithm. This makes the modified algorithm compatible to mine weighted association rules. This approach gives better results than that which treats items uniformly, leading to weighted association rule mining on an encoded temporal database with reduced time and computational complexity. The experimental results are drawn from the complaints database of the telecommunication system. This database technology can be used to enhance the complaint addressing system through the web which is an upgraded technique for the semantic web.

关键词： Temporal database Fuzzy approach Scheduling-aging algorithm triangular membership function Weighted items Weighted association rule mining Weighted support Apriori-Tid algorithm Encoded temporal database

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On the Optimization Methods for Fully Fuzzy Regression Models

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International Journal of Intelligent Technologies and Applied Statistics 2010年第1期3卷 45-55页

作者： Yu-Yun Hsu Hsuan-Ku Liu Ber-Lin Wu General Education Center China University of Technology Taipei Taiwan Department of Mathematics and Information Education National Taipei University of Education Taipei Taiwan Department of Mathematical Sciences National Chengchi University Taipei Taiwan

The goal of this paper is to construct a multiple fuzzy regression model by fuzzy parameters estimation using the fuzzy samples. We propose an optimization model that provides fuzzy coefficients to minimize the distance between the fuzzy regressands and the fuzzy regressors. It concerned with imprecise measurement of observed variables, linear programming estimation and non-parametric methods. This is different from the assumptions as well as the estimation techniques of the classical analysis. Empirical results demonstrate that our new approach is efficient and more realistic than the traditional regression analysis did. [ABSTRACT FROM AUTHOR]

关键词： Fuzzy parameter Fuzzy regression H-cut Methods of least square triangular membership function

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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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A two-stage design of adaptive fuzzy controllers for time-delay systems with unknown models

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2007年第5期38卷 433-449页

作者： Taur, J. S. Lee, G. H. Tao, C. W. Natl Chung Hsing Univ Dept Elect Engn Taichung 402 Taiwan

In this article, a systematic two-stage design method for adaptive fuzzy controllers is presented. The proposed control scheme has low computational complexity. Moreover, the exact mathematical model of the plant to be controlled is not required. The fuzzy controller under consideration is based on the proportional-derivative fuzzy control scheme and triangular membership functions. In the design procedure, the domain intervals of the input and output variables are selected with a heuristic approach to minimize a cost function under the constraint of tolerable overshoots in the response curve. A learning scheme is then proposed to automatically adjust the parameters in the fuzzy controller to reduce the error of the system. It can also be used adaptively to improve the system performance of a time-varying system. Simulations and comparisons are included to demonstrate the effectiveness of the proposed method.

关键词： fuzzy controller if-then rule triangular membership function

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A new approach to fuzzy regression models with application to business cycle analysis

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FUZZY SETS AND SYSTEMS 2002年第1期130卷 33-42页

作者： Wu, B Tseng, NF Natl Chengchi Univ Dept Math Sci & Stat Taipei 11623 Taiwan Natl Chengchi Univ Dept Stat Taipei 11623 Taiwan

Recently, fuzzy regression analysis has been largely applied in the modeling of economic or financial data. However, those data often exhibit certain kinds of linguistic terms, for instance: very good, a little reclining or stable, in the business cycle or the growth rate of GDP, etc. The goal of this paper is to construct a fuzzy regression model by fuzzy parameters estimation using the fuzzy samples. It deals with imprecise measurement of observed variables, fuzzy least square estimation and nonparametric methods. This is different from the assumptions as well as the estimation techniques of the classical analysis. Empirical results demonstrate that our new approach is efficient and more realistic than the traditional regression analysis. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： fuzzy regression fuzzy parameter triangular membership function h-cut methods of least square

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