• Title/Summary/Keyword: Fuzzy regression model

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Design of Fuzzy PID Controller Using GAs and Estimation Algorithm (유전자 알고리즘과 Estimation기법을 이용한 퍼지 제어기 설계)

  • Roh, Seok-Beom;Oh, Sung-Kwun
    • Proceedings of the KIEE Conference
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    • 2001.11c
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    • pp.416-419
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    • 2001
  • In this paper a new approach to estimate scaling factors of fuzzy controllers such as the fuzzy PID controller and the fuzzy PD controller is presented. The performance of the fuzzy controller is sensitive to the variety of scaling factors[1]. The desist procedure dwells on the use of evolutionary computing(a genetic algorithm) and estimation algorithm for dynamic systems (the inverted pendulum). The tuning of the scaling factors of the fuzzy controller is essential to the entire optimization process. And then we estimate scaling factors of the fuzzy controller by means of two types of estimation algorithms such as Neuro-Fuzzy model, and regression polynomial [7]. This method can be applied to the nonlinear system as the inverted pendulum. Numerical studies are presented and a detailed comparative analysis is also included.

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The 24 Hourly Load Forecasting of the Election Day Using the Load Variation Rate (부하변동율을 이용한 선거일의 24시간 수요예측)

  • Song, Kyung-Bin
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.59 no.6
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    • pp.1041-1045
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    • 2010
  • Short-term electric load forecasting of power systems is essential for the power system stability and the efficient power system operation. An accurate load forecasting scheme improves the power system security and saves some economic losses in power system operations. Due to scarcity of the historical same type of holiday load data, most big electric load forecasting errors occur on load forecasting for the holidays. The fuzzy linear regression model has showed good accuracy for the load forecasting of the holidays. However, it is not good enough to forecast the load of the election day. The concept of the load variation rate for the load forecasting of the election day is introduced. The proposed algorithm shows its good accuracy in that the average percentage error for the short-term 24 hourly loads forecasting of the election days is 2.27%. The accuracy of the proposed 24 hourly loads forecasting of the election days is compared with the fuzzy linear regression method. The proposed method gives much better forecasting accuracy with overall average error of 2.27%, which improved about average error of 2% as compared to the fuzzy linear regression method.

The FPNN Algorithm combined with fuzzy inference rules and PNN structure (퍼지추론규칙과 PNN 구조를 융합한 FPNN 알고리즘)

  • Park, Ho-Sung;Park, Byoung-Jun;Ahn, Tae-Chon;Oh, Sung-Kwun
    • Proceedings of the KIEE Conference
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    • 1999.07g
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    • pp.2856-2858
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    • 1999
  • In this paper, the FPNN(Fuzzy Polynomial Neural Networks) algorithm with multi-layer fuzzy inference structure is proposed for the model identification of a complex nonlinear system. The FPNN structure is generated from the mutual combination of PNN (Polynomial Neural Network) structure and fuzzy inference method. The PNN extended from the GMDH(Group Method of Data Handling) uses several types of polynomials such as linear, quadratic and modifled quadratic besides the biquadratic polynomial used in the GMDH. In the fuzzy inference method, simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used Each node of the FPNN is defined as a fuzzy rule and its structure is a kind of fuzzy-neural networks. Gas furnace data used to evaluate the performance of our proposed model.

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An Evaluation of Effectiveness for the Application of Fuzzy Reasoning to Sensory Test (관능검사에 대한 Fuzzy추론 적용의 유효성 평가)

  • Kim, Jeong-Man;Lee, Sang-Do
    • Journal of Korean Society for Quality Management
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    • v.24 no.3
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    • pp.133-144
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    • 1996
  • In order to evaluate the effectiveness of fuzzy reasoning to sensory tests, in this paper, a non-linear fuzzy system model that can estimate the general evaluator obtained from a numerical example of test of taste is constructed. And the applicability of fuzzy reasoning to sensory test is discussed on the basis of errors occurred from the estimates in combination of attributes of objects and from the results of multi-regression analysis. This paper proved that fuzzy reasoning using fuzzy If-then rules is applicable to sensory test.

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Genetically Optimized Fuzzy Polynomial Neural Networks Model and Its Application to Software Process (진화론적 최적 퍼지다항식 신경회로망 모델 및 소프트웨어 공정으로의 응용)

  • Lee, In-Tae;Park, Ho-Sung;Oh, Sung-Kwun;Ahn, Tae-Chon
    • Proceedings of the KIEE Conference
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    • 2004.11c
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    • pp.337-339
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    • 2004
  • In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs). Proceeding the layer, this model creates the optimal network architecture through the selection and the elimination of nodes by itself. So, there is characteristic of flexibility. We use a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. GAs is applied to improve the performance with optimal input variables and number of input variables and order. To evaluate the performance of the GAs-based FPNNs, the models are experimented with the use of Medical Imaging System(MIS) data.

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An adaptive neuro-fuzzy inference system (ANFIS) model to predict the pozzolanic activity of natural pozzolans

  • Elif Varol;Didem Benzer;Nazli Tunar Ozcan
    • Computers and Concrete
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    • v.31 no.2
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    • pp.85-95
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    • 2023
  • Natural pozzolans are used as additives in cement to develop more durable and high-performance concrete. Pozzolanic activity index (PAI) is important for assessing the performance of a pozzolan as a binding material and has an important effect on the compressive strength, permeability, and chemical durability of concrete mixtures. However, the determining of the 28 days (short term) and 90 days (long term) PAI of concrete mixtures is a time-consuming process. In this study, to reduce extensive experimental work, it is aimed to predict the short term and long term PAIs as a function of the chemical compositions of various natural pozzolans. For this purpose, the chemical compositions of various natural pozzolans from Central Anatolia were determined with X-ray fluorescence spectroscopy. The mortar samples were prepared with the natural pozzolans and then, the short term and the long term PAIs were calculated based on compressive strength method. The effect of the natural pozzolans' chemical compositions on the short term and the long term PAIs were evaluated and the PAIs were predicted by using multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) model. The prediction model results show that both reactive SiO2 and SiO2+Al2O3+Fe2O3 contents are the most effective parameters on PAI. According to the performance of prediction models determined with metrics such as root mean squared error (RMSE) and coefficient of correlation (R2), ANFIS models are more feasible than the multiple regression model in predicting the 28 days and 90 days pozzolanic activity. Estimation of PAIs based on the chemical component of natural pozzolana with high-performance prediction models is going to make an important contribution to material engineering applications in terms of selection of favorable natural pozzolana and saving time from tedious test processes.

Analysis of Client Propensity in Cyber Counseling Using Bayesian Variable Selection

  • Pi, Su-Young
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.6 no.4
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    • pp.277-281
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    • 2006
  • Cyber counseling, one of the most compatible type of consultation for the information society, enables people to reveal their mental agonies and private problems anonymously, since it does not require face-to-face interview between a counsellor and a client. However, there are few cyber counseling centers which provide high quality and trustworthy service, although the number of cyber counseling center has highly increased. Therefore, this paper is intended to enable an appropriate consultation for each client by analyzing client propensity using Bayesian variable selection. Bayesian variable selection is superior to stepwise regression analysis method in finding out a regression model. Stepwise regression analysis method, which has been generally used to analyze individual propensity in linear regression model, is not efficient since it is hard to select a proper model for its own defects. In this paper, based on the case database of current cyber counseling centers in the web, we will analyze clients' propensities using Bayesian variable selection to enable individually target counseling and to activate cyber counseling programs.

Fuzzy Polynomial Neural Network Algorithm using GMDH Mehtod and its Application to the Wastewater Treatment Process (GMDH 방법에 의한 FPNN 일고리즘과 폐스처리공정에의 응용)

  • Oh, Sung-Kwon;Hwang, Hyung-Soo;Ahn, Tae-Chon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.7 no.2
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    • pp.96-105
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    • 1997
  • In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed FPNN(Fuzzy Polynomial Neural Network) modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) method and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH method and regression polynomial fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnace and those for wastewater treatment process are used for the purpose of evaluating the performance of the proposed FPNN modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

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Development of Short-Term Load Forecasting Method by Analysis of Load Characteristics during Chuseok Holiday (추석 연휴 전력수요 특성 분석을 통한 단기전력 수요예측 기법 개발)

  • Kwon, Oh-Sung;Song, Kyung-Bin
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.60 no.12
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    • pp.2215-2220
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    • 2011
  • The accurate short-term load forecasting is essential for the efficient power system operation and the system marginal price decision of the electricity market. So far, errors of load forecasting for Chuseok Holiday are very big compared with forecasting errors for the other special days. In order to improve the accuracy of load forecasting for Chuseok Holiday, selection of input data, the daily normalized load patterns and load forecasting model are investigated. The efficient data selection and daily normalized load pattern based on fuzzy linear regression model is proposed. The proposed load forecasting method for Chuseok Holiday is tested in recent 5 years from 2006 to 2010, and improved the accuracy of the load forecasting compared with the former research.

A Plasma-Etching Process Modeling Via a Polynomial Neural Network

  • Kim, Dong-Won;Kim, Byung-Whan;Park, Gwi-Tae
    • ETRI Journal
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    • v.26 no.4
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    • pp.297-306
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    • 2004
  • A plasma is a collection of charged particles and on average is electrically neutral. In fabricating integrated circuits, plasma etching is a key means to transfer a photoresist pattern into an underlayer material. To construct a predictive model of plasma-etching processes, a polynomial neural network (PNN) is applied. This process was characterized by a full factorial experiment, and two attributes modeled are its etch rate and DC bias. According to the number of input variables and type of polynomials to each node, the prediction performance of the PNN was optimized. The various performances of the PNN in diverse environments were compared to three types of statistical regression models and the adaptive network fuzzy inference system (ANFIS). As the demonstrated high-prediction ability in the simulation results shows, the PNN is efficient and much more accurate from the point of view of approximation and prediction abilities.

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