• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.6
• /
• pp.415-424
• /
• 2004

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

• Earthquakes and Structures
• /
• v.24 no.6
• /
• pp.429-437
• /
• 2023

This paper studied the prediction of structural damage indices to buildings after earthquake occurrence using Multiple Linear Regression (MLR) and Fuzzy Linear Regression (FLR) methods. Particularly, the structural damage degree, represented by the Maximum Inter Story Drift Ratio (MISDR), is an essential factor that ensures the safety of the building. Thus, the seismic response of a steel building was evaluated, utilizing 65 seismic accelerograms as input signals. Among the several response quantities, the focus is on the MISDR, which expresses the postseismic damage status. Using MLR and FLR methods and comparing the outputs with the corresponding evaluated by nonlinear dynamic analyses, it was concluded that the FLR method had the most accurate prediction results in contrast to the MLR method. A blind prediction applying a set of another 10 artificial accelerograms also examined the model's effectiveness. The results revealed that the use of the FLR method had the smallest average percentage error level for every set of applied accelerograms, and thus it is a suitable modeling tool in earthquake engineering.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1249-1253
• /
• 2005

This paper presents a method to model the path loss characteristics in microwave urban line-of-sight (LOS) propagation. We propose new upper- and lower-bound models for the LOS path loss using fuzzy linear regression (FLR). The spread of upper- and lower-bound of FLR depends on max and min value of a sample path loss data while the conventional upper- and lower-bound models, the spread of the bound intervals are fixed and do not depend on the sample path loss data. Comparison of our models to conventional upper- and lower-bound models indicate that improvements in accuracy over the conventional models are achieved.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.2D
• /
• pp.233-239
• /
• 2006

Most research for until at now link travel time were research for mean link travel time calculate or estimate which uses the average of the individual vehicle. however, the link travel time distribution is divided caused by with the impact factor which is various traffic condition, signal operation condition and the road conditional etc. preceding study result for link travel time distribution characteristic showed that the patterns of going through traffic were divided up to 2 in the link travel times. therefore, it will be more accurate to divide up the link travel time into the one involving delay and the other without delay, rather than using the average link travel time in terms of assessing the traffic situation. this study is it analyzed transit hour distribution characteristic and a cause using examine to the variables which give an effect at link travel time distribute using simulation program and determinate link travel time distribute ratio estimation model. to assess the distribution of the link travel times, this research develops the regression model and the fuzzy model. the variables that have high level of correlations in both estimation models are the rest time of green ball and the delay vehicles. these variables were used to construct the methods in the estimation models. The comparison of the two estimation models-fuzzy and regression model- showed that fuzzy model out-competed the regression model in terms of reliability and applicability.

• Journal of the Korean Society for Precision Engineering
• /
• v.8 no.3
• /
• pp.27-43
• /
• 1991

연소 가공은 고품질 고정도를 필요로 하는 경우 매우 유효한 가공방법이지만 그 공정이 많은 Parameter에 의해 구성되기 때문에 동일한 조건에서도 정량적인 평가가 어려우므로 작업현장 에서는 과학적 원리와 공학적 지식 보다는 숙련자의 경험과 기능에 의존하고 있는 실정이다. 본 연구에서는 이와 같은 국면에 대처한 문제 해결을 위해 Computer가 인간사고에 근접 할 수 있도록 Fuzzy 이론과 Default 이론을 도입하고 전문가의 이론적 지식과 숙련자의 감각적 지식을 적극 수용 하여 연소용 Expert system (최적 가공 조건의 설정 System과 Trouble shooting system)을 개발하였다. 또한 연소 가공 Data의 불확실한 애매성을 효과적으로 이용 할 수 있도록 Fuzzy 가능성이론에 의해 가공 Datad을 회귀 분석하여 실가공 Data base에 축적시켜 재활용토록 설계하었으며 개발된 본 System 의 실행 결과 그 활용성이 높음을 입증하였다.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.7
• /
• pp.536-543
• /
• 2004

In this paper, we proposed a new method of modeling a neuro-fuzzy system using a hybrid clustering algorithm. The initial parameters and the number of clusters of the proposed system are optimally chosen simultaneously with respect to the process of regression, which is a unique characteristics of the proposed system. The proposed algorithm presented in this work improves the overall performance of the proposed a neuro-fuzzy system by choosing a proper number of clusters adaptively according the characteristics of given data. The process of clustering is performed by deciding on the number of classes, which yields the property of convergence of the system. In experiments, the superiority of the proposed neuro-fuzzy system is demonstrated, especially the process of optimizing parameters and clustering of learning speed.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.2
• /
• pp.267-272
• /
• 2010

This paper suggests an evaluation method through corporate culture's evaluation model considering the relationship and affection between types and elements of corporate culture. 314 data obtained from the members of small and medium enterprises analyzed the relationship by the correlation analysis, and the degree affecting rate the corporate culture types by the regression analysis. Finally, fuzzy system was used to analyze the evaluation model of the corporate culture type. The evaluation model of the corporate culture types in this paper is mixed possibility and necessity sides and showed the usefulness through reviewing the model which has an identification problem of the fuzzy system estimated fuzzy relation matrix for corporate culture types using the model.

• Korea Journal of Hospital Management
• /
• v.10 no.4
• /
• pp.75-97
• /
• 2005

The research objective is to empirically investigate the compensation packages maximizing the utilities of internal customers by applying the market segmentation theory. Data was collected from four Korean hospitals in Seoul, Busan and Gyunggi-do. The research is designed to seek the compensation package maximizing the utility of doctors by mixture regression model, which has been applied as latent structure and other type of finite mixture models from various academic fields since early 1980s. The mixture regression model shows the optimal segments number and fuzzy classification for each observation by EM(expectation-maximization algorism). The finite mixture regression model is to unmix the sample, to identify the groups, and to estimate the parameters of the density function underlying the observed data within each group. The doctors were segmented into 5 groups by their preference for the compensation package. The results of this study imply that the utility of doctors increases with differentiated compensation package segmented by their preference.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.15-15
• /
• 2000

In the past couple of years, there has been increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fused models have been proposed to implement different types of fuzzy reasoning mechanisms and inevitably they suffer from the dimensionality problem when dealing with complex real-world problem. To overcome the problem, we propose the self-organizing networks with activation nodes based on fuzzy inference and polynomial function. The proposed model consists of two parts, one is fuzzy nodes which each node is operated as a small fuzzy system with fuzzy implication rules, and its fuzzy system operates with Gaussian or triangular MF in Premise part and constant or regression polynomials in consequence part. the other is polynomial nodes which several types of high-order polynomials such as linear, quadratic, and cubic form are used and are connected as various kinds of multi-variable inputs. To demonstrate the effectiveness of the proposed method, time series data for gas furnace process has been applied.

• Structural Engineering and Mechanics
• /
• v.45 no.5
• /
• pp.693-702
• /
• 2013

Recently, artificial intelligence tools are most used for structural engineering and mechanics. In order to predict reserve prices and prices of awards, this study proposed a novel regression prediction model by the intelligent Kalman filtering method. An artificial intelligent multiple regression model was established using categorized data and then a prediction model using intelligent Kalman filtering. The rather precise construction bid price model was selected for the purpose of increasing the probability to win bids in the simulation example.