• Title/Summary/Keyword: Weighted Fuzzy Membership Function

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Finding Fuzzy Rules for IRIS by Neural Network with Weighted Fuzzy Membership Function

  • Lim, Joon Shik
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.4 no.2
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    • pp.211-216
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    • 2004
  • Fuzzy neural networks have been successfully applied to analyze/generate predictive rules for medical or diagnostic data. However, most approaches proposed so far have not considered the weights for the membership functions much. This paper presents a neural network with weighted fuzzy membership functions. In our approach, the membership functions can capture the concentrated and essential information that affects the classification of the input patterns. To verify the performance of the proposed model, well-known Iris data set is performed. According to the results, the weighted membership functions enhance the prediction accuracy. The architecture of the proposed neural network with weighted fuzzy membership functions and the details of experimental results for the data set is discussed in this paper.

Extracting Wisconsin Breast Cancer Prediction Fuzzy Rules Using Neural Network with Weighted Fuzzy Membership Functions (가중 퍼지 소속함수 기반 신경망을 이용한 Wisconsin Breast Cancer 예측 퍼지규칙의 추출)

  • Lim Joon Shik
    • The KIPS Transactions:PartB
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    • v.11B no.6
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    • pp.717-722
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    • 2004
  • This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer using neural network with weighted fuzzy membership functions (NNWFM). NNWFM is capable of self-adapting weighted membership functions to enhance accuracy in prediction from the given clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from the enhanced bounded sums of n set of weighted fuzzy membership functions. Two number of prediction rules extracted from NNWFM outperforms to the current published results in number of rules and accuracy with 99.41%.

Minimum Fuzzy Membership Function Extraction for Automatic Premature Ventricular Contraction Detection (자동 조기심실수축 탐지를 위한 최소 퍼지소속함수의 추출)

  • Lim, Joon-Shik
    • Journal of Internet Computing and Services
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    • v.8 no.1
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    • pp.125-132
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    • 2007
  • This paper presents an approach to detect premature ventricular contractions(PVC) using the neural network with weighted fuzzy membership functions(NEWFM), NEWFM classifies normal and PVC beats by the trained weighted fuzzy membership functions using wavelet transformed coefficients extracted from the MIT-BIH PVC database. The eight most important coefficients of d3 and d4 are selected by the non-overlap area distribution measurement method. The selected 8 coefficients are used for 3 data sets showing reliable accuracy rates 99,80%, 99,21%, and 98.78%, respectively, which means the selected input features are less dependent to the data sets. The ECG signal segments and fuzzy membership functions of the 8 coefficients enable input features to interpret explicitly.

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APPLICATIONS OF SIMILARITY MEASURES FOR PYTHAGOREAN FUZZY SETS BASED ON SINE FUNCTION IN DECISION-MAKING PROBLEMS

  • ARORA, H.D.;NAITHANI, ANJALI
    • Journal of applied mathematics & informatics
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    • v.40 no.5_6
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    • pp.897-914
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    • 2022
  • Pythagorean fuzzy sets (PFSs) are capable of modelling information with more uncertainties in decision-making problems. The essential feature of PFSs is that they are described by three parameters: membership function, non-membership function and hesitant margin, with the total of the squares of each parameter equal to one. The purpose of this article is to suggest some new similarity measures and weighted similarity measures for PFSs. Numerical computations have been carried out to validate our proposed measures. Applications of these measures have been applied to some real-life decision-making problems of pattern detection and medicinal investigations. Moreover, a descriptive illustration is employed to compare the results of the proposed measures with the existing analogous similarity measures to show their effectiveness.

Design of a Classifier Based on Supervised Learning Using Fuzzy Membership Function and Weighted Average (퍼지 소속도 함수와 가중치 평균을 이용한 지도 학습 기반 분류기 설계)

  • Woo, Young Woon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.4
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    • pp.508-514
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    • 2021
  • In this paper, to propose a classifier based on supervised learning, three types of fuzzy membership functions that determine the membership of each feature of classification data are proposed. In addition, the possibility of improving the classifier performance was suggested by using the average value calculation method used in the process of deriving the classification result using the average value of the membership degrees for each feature, not by using a simple arithmetic average, but by using a weighted average using various weights. To experiment with the proposed methods, three standard data sets were used: Iris, Ecoli, and Yeast. As a result of the experiment, it was confirmed that evenly excellent classification performance can be obtained for data sets of different characteristics. It was confirmed that better classification performance is possible through improvement of fuzzy membership functions and the weighted average methods.

Identification of Fuzzy-Radial Basis Function Neural Network Based on Mountain Clustering (Mountain Clustering 기반 퍼지 RBF 뉴럴네트워크의 동정)

  • Choi, Jeoung-Nae;Oh, Sung-Kwun;Kim, Hyun-Ki
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.1 no.3
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    • pp.69-76
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    • 2008
  • This paper concerns Fuzzy Radial Basis Function Neural Network (FRBFNN) and automatic rule generation of extraction of the FRBFNN by means of mountain clustering. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values (degree of membership) directly rely on the computation of the relevant distance between data points. Also, we consider high-order polynomial as the consequent part of fuzzy rules which represent input-output characteristic of sup-space. The number of clusters and the centers of clusters are automatically generated by using mountain clustering method based on the density of data. The centers of cluster which are obtained by using mountain clustering are used to determine a degree of membership and weighted least square estimator (WLSE) is adopted to estimate the coefficients of the consequent polynomial of fuzzy rules. The effectiveness of the proposed model have been investigated and analyzed in detail for the representative nonlinear function.

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Multiple Attribute Group Decision Making Problems Based on Fuzzy Number Intuitionistic Fuzzy Information

  • Park, Jin-Han;Kwun, Young-Chel;Park, Jong-Seo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.2
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    • pp.265-272
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    • 2009
  • Fuzzy number intuitionistic fuzzy sets (FNIFSs), each of which is characterized by a membership function and a non-membership function whose values are trigonometric fuzzy number rather than exact numbers, are a very useful means to describe the decision information in the process of decision making. Wang [10] developed some arithmetic aggregation operators, such as the fuzzy number intuitionistic fuzzy weighted averaging (FIFWA) operator, the fuzzy number intuitionistic fuzzy ordered weighted averaging (FIFOWA) operator and the fuzzy number intuitionistic fuzzy hybrid aggregation (FIFHA) operator. In this paper, based on the FIFHA operator and the FIFWA operator, we investigate the group decision making problems in which all the information provided by the decision-makers is presented as fuzzy number intuitionistic fuzzy decision matrices where each of the elements is characterized by fuzzy number intuitionistic fuzzy numbers, and the information about attribute weights is partially known. An example is used to illustrate the applicability of the proposed approach.

The Design of Optimal Fuzzy-Neural networks Structure by Means of GA and an Aggregate Weighted Performance Index (유전자 알고리즘과 합성 성능지수에 의한 최적 퍼지-뉴럴 네트워크 구조의 설계)

  • Oh, Sung-Kwun;Yoon, Ki-Chan;Kim, Hyun-Ki
    • Journal of Institute of Control, Robotics and Systems
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    • v.6 no.3
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    • pp.273-283
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    • 2000
  • In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

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Design of Fuzzy-Neural Networks Structure using Optimization Algorithm and an Aggregate Weighted Performance Index (최적 알고리즘과 합성 성능지수에 의한 퍼지-뉴럴네트워크구조의 설계)

  • Yoon, Ki-Chan;Oh, Sung-Kwun;Park, Jong-Jin
    • Proceedings of the KIEE Conference
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    • 1999.07g
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    • pp.2911-2913
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    • 1999
  • This paper suggest an optimal identification method to complex and nonlinear system modeling that is based on Fuzzy-Neural Network(FNN). The FNN modeling implements parameter identification using HCM algorithm and optimal identification algorithm structure combined with two types of optimization theories for nonlinear systems, we use a HCM Clustering Algorithm to find initial parameters of membership function. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using optimal identification algorithm. The proposed optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregate objective function(performance index) with weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

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T-S fuzzy PID control based on RCGAs for the automatic steering system of a ship (선박자동조타를 위한 RCGA기반 T-S 퍼지 PID 제어)

  • Yu-Soo LEE;Soon-Kyu HWANG;Jong-Kap AHN
    • Journal of the Korean Society of Fisheries and Ocean Technology
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    • v.59 no.1
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    • pp.44-54
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    • 2023
  • In this study, the second-order Nomoto's nonlinear expansion model was implemented as a Tagaki-Sugeno fuzzy model based on the heading angular velocity to design the automatic steering system of a ship considering nonlinear elements. A Tagaki-Sugeno fuzzy PID controller was designed using the applied fuzzy membership functions from the Tagaki-Sugeno fuzzy model. The linear models and fuzzy membership functions of each operating point of a given nonlinear expansion model were simultaneously tuned using a genetic algorithm. It was confirmed that the implemented Tagaki-Sugeno fuzzy model could accurately describe the given nonlinear expansion model through the Zig-Zag experiment. The optimal parameters of the sub-PID controller for each operating point of the Tagaki-Sugeno fuzzy model were searched using a genetic algorithm. The evaluation function for searching the optimal parameters considered the route extension due to course deviation and the resistance component of the ship by steering. By adding a penalty function to the evaluation function, the performance of the automatic steering system of the ship could be evaluated to track the set course without overshooting when changing the course. It was confirmed that the sub-PID controller for each operating point followed the set course to minimize the evaluation function without overshoot when changing the course. The outputs of the tuned sub-PID controllers were combined in a weighted average method using the membership functions of the Tagaki-Sugeno fuzzy model. The proposed Tagaki-Sugeno fuzzy PID controller was applied to the second-order Nomoto's nonlinear expansion model. As a result of examining the transient response characteristics for the set course change, it was confirmed that the set course tracking was satisfactorily performed.