• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1183-1188
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• 2004

The inverse heat conduction problem (IHCP) is a problem of estimating boundary condition from temperature measurement at one or more interior points. Neural networks are general information processing systems inspired by the connectionist theory of human brain. By properly training the network by the learning rule, the neural network method can handle many non-linear or other complex problems. In this work, neural network is applied to complicated inverse heat conduction problems. Efficiency of the procedure is enhanced by incorporating the radial basis functions (RBF). The RBF is trained faster than other neural network and can find smooth solution. In order to demonstrate the effectiveness of the current scheme, a typical one-dimensional IHCP is considered. At one surface, the temperature as well as the heat flux is known. The unknown temperature of interest is estimated on the other side of the slab. The results from the proposed method based on RBF neural network are compared with the conventional method.

• Agricultural and Biosystems Engineering
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• v.1 no.2
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• pp.67-72
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• 2000

A dynamic draft model is necessary to analyze mechanics of tillage and to design optimal tillage tools. In order to deal with draft dynamics, a neural network paradigm was applied to develop dynamic draft models. For the development of the models, three kinds of tillage tools were used to measure drafts in the soil bin and a time lagged recurrent neural network was developed. The neural network had a structure to predict dynamic draft, having a function of one-step-ahead prediction. A procedure for network prediction model identification was established. The results show promising modeling of the dynamic drafts with developed neural network.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.10
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• pp.1121-1232
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• 1990

A hierarchical neural network structure is described for robot PTP trajectory planning. In the first level, the multi-layered Perceptron neural network is used for the inverse kinematics with the back-propagation learning procedure. In the second level, a saccade generation model based joint trajectory planning model in proposed and analyzed with several features. Various simulations are performed to investigate the characteristics of the proposed neural networks.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.4
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• pp.69-87
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• 2006

This paper proposes an effective approach for the part-machine grouping(PMG) based on the non-binary part-machine incidence matrix in which real manufacturing factors such as the operation sequences with multiple visits to the same machine and production volumes of parts are incorporated and each entry represents actual moves due to different operation sequences. The proposed approach adopts Fuzzy ART neural network to quickly create the Initial part families and their machine cells. A new performance measure to evaluate and compare the goodness of non-binary block diagonal solution is suggested. To enhance the poor solution due to category proliferation inherent to most artificial neural networks, a supplementary procedure reassigning parts and machines is added. To show effectiveness of the proposed approach to large-size PMG problems, a psuedo-replicated clustering procedure is designed. Experimental results with intermediate to large-size data sets show effectiveness of the proposed approach.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.4
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• pp.574-581
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• 1996

The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Networks(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Self-Organized Distributed Networks (SODN). The learning with the SODN is fast and precise. Such properties are caused from the local learning mechanism. Each local network learns only data in a subregion. This paper also discusses neural network as identifier of nonlinear dynamical systems. The structure of nonlinear system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems. (author). 13 refs., 7 figs., 2 tabs.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.156.3-156
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• 2001

This paper handles synthesis and investigation of a neural network guidance law based on pursuit-evasion games. This work considers two-dimensional pursuit-evasion games solved by using the gradient method. The procedure of developing a guidance law from the game-optimal solutions is deeply examined, and important features of the neural network guidance are investigated. The proposed neural network guidance law takes the range, range rate, line-of-sight rate, and heading error as its input variables. By reconstructing the trajectory, the accuracy of the neural network approximation is verified. Afterwards, robustness of the neural network guidance to the autopilot lag, which results from its feedback structure, is investigated ...

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.11
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• pp.51-57
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• 2014

This paper presents an algorithmic type computing technique of process coefficient in predicting model of temperature for reheating furnace and also suggests a design method of neural network model to find an adequate value of process coefficient for arbitrary operating conditions including test conditons. The proposed neural network use furnace temperature, line speed and slab information as input variables, and process coefficient is output variable. Reasonable process coefficients can be obtained by an algorithmic procedure proposed in this paper using process data gathered at test conditons. Also, neural network model output equal process coefficient under same input conditions. This means that adquate process coefficients can be found by only computing neural network model without additive test even if operating conditions vary.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.95-101
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• 1999

In this paper, a comparison between different methods to combine predictions from neural networks will be given. These methods are bagging, bumping, and balancing. Those are based on the analysis of the ensemble generalization error into an ambiguity term and a term incorporating generalization performances of individual networks. Neural Networks and AI machine learning models are prone to overfitting. A strategy to prevent a neural network from overfitting, is to stop training in early stage of the learning process. The complete data set is spilt up into a training set and a validation set. Training is stopped when the error on the validation set starts increasing. The stability of the networks is highly dependent on the division in training and validation set, and also on the random initial weights and the chosen minimization procedure. This causes early stopped networks to be rather unstable: a small change in the data or different initial conditions can produce large changes in the prediction. Therefore, it is advisable to apply the same procedure several times starting from different initial weights. This technique is often referred to as training ensembles of neural networks. In this paper, we presented a comparison of three statistical methods to prevent overfitting of neural network.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.5
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• pp.966-972
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• 2007

This paper presents position control of nonlinear three-dimensional crane systems using neural network approach. Such crane system generally includes very complicated characteristic dynamics and mechanical framework such that its mathematical model is expressed by strong nonlinearity. This leads difficulty in control design for the systems. We linearize the nonlinear system model to construct PID control applying well-known linear control theory and then neural network is utilized to compensate system perturbation due to linearization. Thus, control input of the crane system is composed of nominal PID and neural output signals respectively. Our method illustrates simple design procedure, but system perturbation and modelling error are overcome through a neural compensator. As well. adaptive neural control is constructed from online learning. Computer simulation demonstrates our control approach is superior to the classic control systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.118-124
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• 2004

In this paper, we present a indirect adaptive control method using a wavelet neural network (WNN) for the control of chaotic nonlinear systems without precise mathematical models. The proposed indirect adaptive control method includes the off-line identification and on-line control procedure for chaotic nonlinear systems. In the off-line identification procedure, the WNN based identification model identifies the chaotic nonlinear system by using the serial-parallel identification structure and is trained by the gradient-descent method. And, in the on-line control procedure, a WNN controller is designed by using the off-line identification model and is trained by the error back-propagation algorithm. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with applications to the chaotic nonlinear systems.