• IE interfaces
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• v.16 no.spc
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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.

• 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.

• 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.

• 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.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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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.

• 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.

• Journal of IKEEE
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• v.22 no.3
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• pp.866-869
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• 2018

This paper presents the analysis of the human driving behavior based on the expression as a GMDH technique focusing on the driver's collision avoidance maneuver. The driving data are collected by using the three dimensional driving simulator based on CAVE, which provides stereoscopic immersive vision. A GMDH is also introduced and applied to the measured data in order to build a mathematical model of driving behavior. From the obtained model, it is found that the longitudinal distance between cars($x_1$), the longitudinal relative velocity($x_2$) and the lateral displacement between cars($x_4$) play important roles in the collision avoidance maneuver under the 3D environments.

• 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.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.326-328
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• 1993

Modelling theory, based on differential equations, is not an adequate tool for solving the problems of complex system. Identification of complex system using GMDH(group method of data handling) is more appropriate for this problems. In this paper, GMDH algorithm is used to identify the nonlinear plant and to design model following nonlinear control system. Simulation for the DC motor show the good performance of model following nonlinear control system.