• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2007년도 추계학술대회 및 정기총회
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• pp.183-189
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• 2007

In this paper, we developed the fuzzy group method data handling-type (GMDH) Model and applied it to demand forecasting of educational expenses. At present, GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to fuzzy system, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called as the fuzzy GMDH. The fuzzy GMDH-type networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the fuzzy GMDH. A computer program is developed and successful applications are shown in the field of demand forecasting problem of educational expenses with the number of factors considered.

• 한국정보통신학회논문지
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• 제14권3호
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• pp.559-564
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• 2010

복잡한 비선형 시스템을 예측하기 위하여 GMDH(Group Method of Data Handling) 알고리듬을 사용할 수 있다. 기존의 GMDH 알고리듬은 정해진 절차에 의해 입력층부터 중간층들을 거쳐 출력층에서 시스템의 예측 출력을 생성한다. 각 층의 출력은 전 층의 출력에 의해서만 생성된다. 그러나 전형적인 GMDH 알고리듬에서 층별로 최적의 구조가 결정되지만 전체적으로는 최적의 구조가 결정되지 않을 수도 있다는 문제점을 해결하기 위해 예측된 출력을 실제의 출력과 비교하여 그 에러를 피드백하여 전체적으로 최적의 구조를 가지는 GMDH 예측 모델을 구성함으로 써 보다 정확한 예측이 가능하도록 하였다. 제안된 알고리듬이 기존의 알고리듬보다 성능이 향상된 것을 컴퓨터 시뮬레이션을 통해 검증하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.1044-1047
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• 1996

In this paper, GMDH(Group Method of Data Handling) algorithm, which is based on heuristic self organization to predict and identify the complex system, is applied to the control system of DC servo motor. The mathematical relation between input voltage and motor speed is obtained by GMDH algorithm. A design method of model following control system based on GMDH algorithm is developed. As a result of applying this method to DC servo motor, the simulation and experiment have shown that the developed method gives a good performance in tracking the reference model and in rejection of disturbance, in spite of constant load and changing load.

• 산업공학
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• 제16권spc호
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• pp.28-32
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• 2003

In this paper, the fuzzy group method data handling-type(GMDH) neural networks and their application to the forecasting of mobile communication system are described. At present, GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to neural networks, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of neuro-fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called as the neuro-fuzzy GMDH. The GMDH-type neural networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the neuro-fuzzy GMDH. The computer program is developed and successful applications are shown in the field of estimating problem of mobile communication with the number of factors considered.

• Geomechanics and Engineering
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• 제33권2호
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• pp.183-194
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• 2023

This study presents the prediction of the California bearing ratio (CBR) of coarse- and fine-grained soils using artificial intelligence technology. The group method of data handling (GMDH) algorithm, an artificial neural network-based model, was used in the prediction of the CBR values. In the design of the prediction models, various combinations of independent input variables for both coarse- and fine-grained soils have been used. The results obtained from the designed GMDH-type neural networks (GMDH-type NN) were compared with other regression models, such as linear, support vector, and multilayer perception regression methods. The performance of models was evaluated with a regression coefficient (R²), root-mean-square error (RMSE), and mean absolute error (MAE). The results showed that GMDH-type NN algorithm had higher performance than other regression methods in the prediction of CBR value for coarse- and fine-grained soils. The GMDH model had an R² of 0.938, RMSE of 1.87, and MAE of 1.48 for the input variables {G, S, and MDD} in coarse-grained soils. For fine-grained soils, it had an R² of 0.829, RMSE of 3.02, and MAE of 2.40, when using the input variables {LL, PI, MDD, and OMC}. The performance evaluations revealed that the GMDH-type NN models were effective in predicting CBR values of both coarse- and fine-grained soils.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.153-158
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• 1995

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed fuzzy GMDH modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) algorithm 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 algorithm and fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnaceare those for sewage treatment process are used for the purpose of evaluating the performance of the proposed fuzzy GMDH modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• Steel and Composite Structures
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• 제42권1호
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• pp.123-137
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• 2022

The fundamental period (FP) is one of the most critical parameters for the seismic design of structures. In the reinforced concrete (RC) infilled frame, the infill walls significantly affect the FP because they change the stiffness and mass of the structure. Although several formulas have been proposed for estimating the FP of the RC infilled frame, they are often associated with high bias and variance. In this study, an efficient soft computing model, namely the group method of data handling (GMDH), is proposed to predict the FP of regular RC infilled frames. For this purpose, 4026 data sets are obtained from the open literature, and the quality of the database is examined and evaluated in detail. Based on the cleaning database, several GMDH models are constructed and the best prediction model, which considers the height of the building, the span length, the opening percentage, and the infill wall stiffness as the input variables for predicting the FP of regular RC infilled frames, is chosen. The performance of the proposed GMDH model is further underscored through comparison of its FP predictions with those of existing design codes and empirical models. The accuracy of the proposed GMDH model is proven to be superior to others. Finally, explicit formulas and a graphical user-friendly interface (GUI) tool are developed to apply the GMDH model for practical use. They can provide a rapid prediction and design for the FP of regular RC infilled frames.

• 한국정보통신학회논문지
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• 제13권6호
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• pp.1200-1207
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• 2009

복잡한 비선형 시스템을 모델링하기 위하여 GMDH(Group Method of Data Handling) 알고리듬을 사용할 수 있다. 기존의 GMDH 알고리듬은 정해진 절차에 의해 입력층부터 중간층들을 거쳐 출력층에서 시스템의 모델링 출력을 생성한다. 각 층의 출력은 전 층의 출력에 의해서만 생성된다. 그러나 입력들 중에서는 다른 입력들보다 모델링 결과에 더 큰 영향을 줄 수 있는 입력들이 있을 수 있다. 따라서 본 논문에서는 영향이 큰 입력들을 층간 연결하여 모델링 성능을 향상시키는 방법을 제안하였다. 제안된 알고리듬이 기존의 알고리듬보다 성능이 향상된 것을 컴퓨터 시뮬레이션을 통해 검증하였다.

• Computers and Concrete
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• 제22권2호
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• pp.197-207
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• 2018

In the present study, group method of data handling networks (GMDH) are adopted and evaluated for shear strength prediction of both FRP-reinforced concrete members with and without stirrups. Input parameters considered for the GMDH are altogether 12 influential geometrical and mechanical parameters. Two available and very recently collected comprehensive datasets containing 112 and 175 data samples are used to develop new models for two cases with and without shear reinforcement, respectively. The proposed GMDH models are compared with several codes of practice. An artificial neural network (ANN) model and an ANFIS based model are also developed using the same databases to further assessment of GMDH. The accuracy of the developed models is evaluated by statistical error parameters. The results show that the GMDH outperforms other models and successfully can be used as a practical and effective tool for shear strength prediction of members without stirrups ($R^2=0.94$) and with stirrups ($R^2=0.95$). Furthermore, the relative importance and influence of input parameters in the prediction of shear capacity of reinforced concrete members are evaluated through parametric and sensitivity analyses.

• 품질경영학회지
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• 제14권1호
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• pp.53-60
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• 1986

The purpose of this study has been finding where GMDH (Group Method of Data Handling) lies in accordance with comparing other methods and ascertaining the effectiveness of GMDH at the systems of forecasting method. Other methods used for the comparison are: multiple regression model, Brown's third exponential smoothing model. Also the study has reviewed how the expected value and equatior are changed by GMDH. At the same time, the study has also reviewed various characteristics made with comparatively a few data. In conclusion, GMDH is better than the other method in point of view fitness, high effectiveness in self-selection and self-construction of the variables.