• Title/Summary/Keyword: group method of data handling (GMDH)

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Analyzing nuclear reactor simulation data and uncertainty with the group method of data handling

  • Radaideh, Majdi I.;Kozlowski, Tomasz
    • Nuclear Engineering and Technology
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    • v.52 no.2
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    • pp.287-295
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    • 2020
  • Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems. In this study, multi-layer GMDH networks are used to perform uncertainty quantification (UQ) and sensitivity analysis (SA) of nuclear reactor simulations. GMDH is utilized as a surrogate/metamodel to replace high fidelity computer models with cheap-to-evaluate surrogate models, which facilitate UQ and SA tasks (e.g. variance decomposition, uncertainty propagation, etc.). GMDH performance is validated through two UQ applications in reactor simulations: (1) low dimensional input space (two-phase flow in a reactor channel), and (2) high dimensional space (8-group homogenized cross-sections). In both applications, GMDH networks show very good performance with small mean absolute and squared errors as well as high accuracy in capturing the target variance. GMDH is utilized afterward to perform UQ tasks such as variance decomposition through Sobol indices, and GMDH-based uncertainty propagation with large number of samples. GMDH performance is also compared to other surrogates including Gaussian processes and polynomial chaos expansions. The comparison shows that GMDH has competitive performance with the other methods for the low dimensional problem, and reliable performance for the high dimensional problem.

Modeling of Nonlinear Dynamic Dynamic Systems Using a Modified GMDH Algorithm (수정된 GMDH 알고리즘을 이용한 비선형 동적 시스템의 모델링)

  • 홍연찬;엄상수
    • Journal of the Korean Institute of Intelligent Systems
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    • v.8 no.3
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    • pp.50-55
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    • 1998
  • The GMDH(Group Method of Data Handling) is a useful data analysis technique for identification of nonlinear complex systems. Therefore, in this paper the application method of GMDH algorithm for modeling nonlinear dynamic systems is proposed. The identification of dynamic systems by using GMDH consists of applying a set of input/output data and computing the necessary coefficient set dynamically. Also, in this paper, by reducing sequentially the criterion which can adopt or reject the data, a method to prevent excessive computation that is a disadvantage of GMDH is proposed.

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A Study on the Accuracy of the Forecasting Using Group Method of Data Handling (자료(資料)취급의 집단적 방법(GMDH)을 사용한 자측(子測)의 정도(精度)에 관한 연구(硏究))

  • Jo, Am
    • Journal of Korean Society for Quality Management
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    • v.14 no.1
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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.

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A Study onthe Modelling and control Using GMDH Algorithm (GMDH 알고리즘을 이용한 모델링 및 제어에 관한 연구)

  • 최종헌;홍연찬
    • Journal of the Korean Institute of Intelligent Systems
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    • v.7 no.3
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    • pp.65-71
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    • 1997
  • With the emergence of neural network, there is a revived interest in identification of nonlinear systems. So in this paper, to identify unknown nonlinear systems dynamically we propose DPNN(Dynamic Polynomial Neural Network) using GMDH (Group Method of Data Handling) algorithm. The dynamic system identification using GMDH consists of applying a set of inputloutput data to train the network by dynamically computing the necessary coeffici1:nt sets. Then, MRAC(Mode1 Reference Adaptive Control) is designed to control nonlinear systems using DPNN. In the result, we can see that the modelling and control using DPNN work well by computer simulation.

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A Study on the Performance Improvement of GMDH Algorithm by Feedback (피드백에 의한 GMDH 알고리듬 성능 향상에 관한 연구)

  • Hong, Yeon-Chan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.14 no.3
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    • pp.559-564
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    • 2010
  • The GMDH(Group Method of Data Handling) algorithm can be used to predict the complex nonlinear systems. The traditional GMDH algorithm produces the prdicted output of the system model in the output layer through the input layer and the intermediate layers as the prescribed process. The outputs of each layer are produced only by the outputs of the former layer. However, in the traditional GMDH algorithm, though the optimal structure of each layer is derived, the overall structure may not be derived optimally. To overcome this problem, GMDH prediction model which has the overall optimal structure is constructed by feeding back the error between the predicted output and the real output. This can make the prediction more precise. The capability improvement of the proposed algorithm compared to the traditional algorithm is verified through computer simulation.

Prediction of California bearing ratio (CBR) for coarse- and fine-grained soils using the GMDH-model

  • Mintae Kim;Seyma Ordu;Ozkan Arslan;Junyoung Ko
    • Geomechanics and Engineering
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    • v.33 no.2
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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.

Improvement of Modeling Capability of GMDH Algorithm with Interlayer Connection (층간 연결에 의한 GMDH 알고리듬의 모델링 성능 향상)

  • Hong, Yeon-Chan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.6
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    • pp.1200-1207
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    • 2009
  • The GMDH(Group Method of Data Handling) algorithm can be used to model the complex nonlinear systems. The traditional GMDH algorithm produces the output of the system model in the output layer through the input layer and the intermediate layers as the prescribed process. The outputs of each layer are produced only by the outputs of the former layer. However among the inputs there may be the inputs which can influence the modeling result more than the other inputs. Therefore in this paper the method which improve the modeling capability by interlayer connection of more influential inputs is proposed. The capability improvement of the proposed algorithm compared to the traditional algorithm is verified through computer simulation.

Application of GMDH model for predicting the fundamental period of regular RC infilled frames

  • Tran, Viet-Linh;Kim, Seung-Eock
    • Steel and Composite Structures
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    • v.42 no.1
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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.

Performance Improvement of Nonlinear System Modeling Using GMDH (GMDH를 이용한 비선형 시스템의 모델링 성능 개선)

  • Hong, Yeon-Chan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.14 no.7
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    • pp.1544-1550
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    • 2010
  • There have been many researches applying GMDH for modelling nonlinear dynamic systems. However, these methods require a great amount of computation in return of the accuracy. Thus, in this paper, we propose a method to reduce the amount of computation in GMDH by adjusting the adopting criterion of input data in decrement while at least maintaining the accuracy. The simulation result verifies that the proposed method can successfully reduce the amount of computation without the expense of the error rate, if not significantly better.

Fuzzy GMDH Model and Its Application to the Sewage Treatment Process (퍼지 GMDH 모델과 하수처리공정에의 응용)

  • 노석범;오성권;황형수;박희순
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1995.10b
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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.

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