• Title/Summary/Keyword: Fuzzy c-regression

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Fuzzy c-Regression Using Weighted LS-SVM

  • Hwang, Chang-Ha
    • 한국데이터정보과학회:학술대회논문집
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    • 2005.10a
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    • pp.161-169
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    • 2005
  • In this paper we propose a fuzzy c-regression model based on weighted least squares support vector machine(LS-SVM), which can be used to detect outliers in the switching regression model while preserving simultaneous yielding the estimates of outputs together with a fuzzy c-partitions of data. It can be applied to the nonlinear regression which does not have an explicit form of the regression function. We illustrate the new algorithm with examples which indicate how it can be used to detect outliers and fit the mixed data to the nonlinear regression models.

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Switching Regression Analysis via Fuzzy LS-SVM

  • Hwang, Chang-Ha
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.2
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    • pp.609-617
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    • 2006
  • A new fuzzy c-regression algorithm for switching regression analysis is presented, which combines fuzzy c-means clustering and least squares support vector machine. This algorithm can detect outliers in switching regression models while yielding the simultaneous estimates of the associated parameters together with a fuzzy c-partitions of data. It can be employed for the model-free nonlinear regression which does not assume the underlying form of the regression function. We illustrate the new approach with some numerical examples that show how it can be used to fit switching regression models to almost all types of mixed data.

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FUZZY REGRESSION MODEL WITH MONOTONIC RESPONSE FUNCTION

  • Choi, Seung Hoe;Jung, Hye-Young;Lee, Woo-Joo;Yoon, Jin Hee
    • Communications of the Korean Mathematical Society
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    • v.33 no.3
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    • pp.973-983
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    • 2018
  • Fuzzy linear regression model has been widely studied with many successful applications but there have been only a few studies on the fuzzy regression model with monotonic response function as a generalization of the linear response function. In this paper, we propose the fuzzy regression model with the monotonic response function and the algorithm to construct the proposed model by using ${\alpha}-level$ set of fuzzy number and the resolution identity theorem. To estimate parameters of the proposed model, the least squares (LS) method and the least absolute deviation (LAD) method have been used in this paper. In addition, to evaluate the performance of the proposed model, two performance measures of goodness of fit are introduced. The numerical examples indicate that the fuzzy regression model with the monotonic response function is preferable to the fuzzy linear regression model when the fuzzy data represent the non-linear pattern.

Self-Organizing Fuzzy Modeling Based on Hyperplane-Shaped Clusters (다차원 평면 클러스터를 이용한 자기 구성 퍼지 모델링)

  • Koh, Taek-Beom
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.12
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    • pp.985-992
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    • 2001
  • This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

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Fuzzy c-Logistic Regression Model in the Presence of Noise Cluster

  • Alanzado, Arnold C.;Miyamoto, Sadaaki
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2003.09a
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    • pp.431-434
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    • 2003
  • In this paper we introduce a modified objective function for fuzzy c-means clustering with logistic regression model in the presence of noise cluster. The logistic regression model is commonly used to describe the effect of one or several explanatory variables on a binary response variable. In real application there is very often no sharp boundary between clusters so that fuzzy clustering is often better suited for the data.

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Self-Organizing Fuzzy Modeling Using Creation of Clusters (클러스터 생성을 이용한 자기구성 퍼지 모델링)

  • Koh, Taek-Beom
    • Journal of the Korean Institute of Intelligent Systems
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    • v.12 no.4
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    • pp.334-340
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    • 2002
  • This paper proposes a self-organizing fuzzy modeling which can create a new hyperplane-shaped cluster by applying multiple regression to input/output data with relatively large fuzzy entropy, add the new cluster to fuzzy rule base and adjust parameters of the fuzzy model in repetition. Tn the coarse tuning, weighted recursive least squared algorithm and fuzzy C-regression model clustering are used and in the fine tuning, gradient descent algorithm is used to adjust parameters of the fuzzy model precisely And learning rates are optimized by utilizing meiosis-genetic algorithm. To check the effectiveness and feasibility of the suggested algorithm, four representative examples for system identification are examined and the performance of the identified fuzzy model is demonstrated in comparison with that of the conventional fuzzy models.

A SOFT-SENSING MODEL FOR FEEDWATER FLOW RATE USING FUZZY SUPPORT VECTOR REGRESSION

  • Na, Man-Gyun;Yang, Heon-Young;Lim, Dong-Hyuk
    • Nuclear Engineering and Technology
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    • v.40 no.1
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    • pp.69-76
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    • 2008
  • Most pressurized water reactors use Venturi flow meters to measure the feedwater flow rate. However, fouling phenomena, which allow corrosion products to accumulate and increase the differential pressure across the Venturi flow meter, can result in an overestimation of the flow rate. In this study, a soft-sensing model based on fuzzy support vector regression was developed to enable accurate on-line prediction of the feedwater flow rate. The available data was divided into two groups by fuzzy c means clustering in order to reduce the training time. The data for training the soft-sensing model was selected from each data group with the aid of a subtractive clustering scheme because informative data increases the learning effect. The proposed soft-sensing model was confirmed with the real plant data of Yonggwang Nuclear Power Plant Unit 3. The root mean square error and relative maximum error of the model were quite small. Hence, this model can be used to validate and monitor existing hardware feedwater flow meters.

Polynomial Fuzzy Radial Basis Function Neural Network Classifiers Realized with the Aid of Boundary Area Decision

  • Roh, Seok-Beom;Oh, Sung-Kwun
    • Journal of Electrical Engineering and Technology
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    • v.9 no.6
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    • pp.2098-2106
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    • 2014
  • In the area of clustering, there are numerous approaches to construct clusters in the input space. For regression problem, when forming clusters being a part of the overall model, the relationships between the input space and the output space are essential and have to be taken into consideration. Conditional Fuzzy C-Means (c-FCM) clustering offers an opportunity to analyze the structure in the input space with the mechanism of supervision implied by the distribution of data present in the output space. However, like other clustering methods, c-FCM focuses on the distribution of the data. In this paper, we introduce a new method, which by making use of the ambiguity index focuses on the boundaries of the clusters whose determination is essential to the quality of the ensuing classification procedures. The introduced design is illustrated with the aid of numeric examples that provide a detailed insight into the performance of the fuzzy classifiers and quantify several essentials design aspects.

A Study on Fault Detection using Fuzzy Trend Monitoring Technique of UAV Turbofan Engine (퍼지 경향 감시 기법을 이용한 무인기용 터보팬 엔진의 손상 탐지에 관한 연구)

  • Kong, C.D.;Kho, S.H.;Ki, J.Y.;Kho, H.Y.;Oh, S.H.;Kim, J.H.
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • 2007.11a
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    • pp.345-349
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    • 2007
  • In this study a fuzzy trend monitoring method for detecting the engine mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration. etc. Using engine condition data set as a input which generated by linear regression analysis of real engine instrument data, an application of fuzzy logic in diagnostics estimate a cause of fault in each components.

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Neuro-Fuzzy System and Its Application Using CART Algorithm and Hybrid Parameter Learning (CART 알고리즘과 하이브리드 학습을 통한 뉴로-퍼지 시스템과 응용)

  • Oh, B.K.;Kwak, K.C.;Ryu, J.W.
    • Proceedings of the KIEE Conference
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    • 1998.07b
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    • pp.578-580
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    • 1998
  • The paper presents an approach to the structure identification based on the CART (Classification And Regression Tree) algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy system. By using the CART algorithm, the proposed method can roughly estimate the numbers of membership function and fuzzy rule using the centers of decision regions. Then the parameter identification is carried out by the hybrid learning scheme using BP (Back-propagation) and RLSE (Recursive Least Square Estimation) from the numerical data. Finally, we will show it's usefulness for fuzzy modeling to truck backer upper control.

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