• Journal of the Society of Naval Architects of Korea
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• v.34 no.2
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• pp.56-63
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• 1997

This paper is concerned with the design of neural-network based autopilot control system. In this paper, the back-propagation algorithm is introduced and explained in detail. The system identification method based on neural networks for ship motion is developed and its efficacy is verified by using a simple ship maneuvering model. Problems which may arise in a complex maneuvering model are then discussed. The neural-network based system identification method developed in this paper can be used effectively for reconstructing the ship maneuvering moodel which is known to have nonlinearity.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.133-136
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• 2008

In this paper, ALOS-AVNIR, PRISM, and JERS-1 images are used in a multilayer perceptron neural network (MLPNN) that relates them to forest variable measurements on the ground. The structure of this MLPNN is a three layers neural network that contains eight input neurons, 10 hidden neurons and five output neurons. It is shown that the biomass estimation accuracy is significantly improved when the MLPNN is used in comparison with Maximum Likelihood algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.30-35
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• 1990

Learning control of a robot manipulator is proposed using the backpropagation neural network. The learning controller is composed of both a linear feedback controller and a neural network-based feedforward controller. The stability analysis of the learning controller is presented. Three energy functions are selected in teaching the neural network controller : 1/2.SIGMA.vertical bar torque error vertical bar $^{2}$, 1/2.SIGMA..alpha. vertical bar position error vertical bar $^{2}$ + .betha. vertical bar velocity error vertical bar $^{2}$ + .gamma. vertical bar acceleration error vertical bar $^{2}$ and learning methods are presented. Simulation results show that the learning controller which is learned to minimize the third energy function performs better than the others in tracking problems. Some properties of the learning controller are discussed with simulation results.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.675-678
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• 1997

In this research we continue the study of nuclear power plant steam generator's intelligent modeling. This model represents the input-output behavior and is a preliminary stage for intelligent control. Among many intelligent models available, we study neural network models that have been proven as universal function approximators. We select multilayer perceptrons, circular backpropagation networks, piecewise linearly trained networks and recurrent neural networks as the candidates for the steam generator's intelligent models. We take the input-output pairs from steam generator's reference model and train the neural network models. We validate trained neural network models as intelligent models of steam generator.

• Journal of the Korean GEO-environmental Society
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• v.19 no.5
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• pp.13-22
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• 2018

The number of researches on the mechanical properties of cemented sand and gravel (CSG) materials and the application of the CSG Dam has been increased. In order to explain the technical scheme of strength prediction model about the artificial neural network, we obtained the sample data by orthogonal test using the PVA (Polyvinyl alcohol) fiber, different amount of cementing materials and age, and established the efficient evaluation and prediction system. Combined with the analysis about the importance of influence factors, the prediction accuracy was above 95%. This provides the scientific theory for the further application of CSG, and will also be the foundation to apply the artificial neural network theory further in water conservancy project for the future.

• Journal of applied mathematics & informatics
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• v.35 no.3_4
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• pp.387-397
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• 2017

In order to react the dynamic behavior of the system more actually, it is necessary to solve the first problem of synchronization for Markovian jump complex network system in practical engineering problem. In this paper, the problem of local stochastic synchronization for Markovian nonlinear coupled neural network system is investigated, including nonlinear coupling terms and mode-dependent delays, that is less restriction to other system. By designing the Lyapunov-Krasovskii functional and applying less conservative inequality, we get a new criterion to ensure local synchronization in mean square for Markovian nonlinear coupled neural network system. The criterion introduced some free matrix variables, which are less conservative. The simulation confirmed the validity of the conclusion.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.10
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• pp.446-452
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• 2002

For most of pattern recognition applications, it is required to correctly recognize patterns even if they have translation variations. In this paper, to achieve the goal of translation invariant pattern recognition, we propose a new generalized translation invariant second-order neural network using a constraint on the weights. The weight constraint is implemented using generalized translation invariant features which are accumulated sums of pixel combinations. Simulation results will be given to demonstrate that the proposed second-order neural network has the generalized translation invariant property.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.174-178
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• 2002

This paper discusses a blind equalization technique for FIR channel system, that might be minimum phase or not, in digital communication. The proposed techniques consist of two parts. One is to estimate the original channel coefficients based on fourth-order cumulants of the channel output, the other is to employ RBF neural network to model an inverse system fur the original channel. Here, the estimated channel is used as a reference system to train the RBF. The proposed RBF equalizer provides fast and easy teaming, due to the structural efficiency and excellent recognition-capability of R3F neural network. Throughout the simulation studies, it was found that the proposed blind RBF equalizer performed favorably better than the blind MLP equalizer, while requiring the relatively smaller computation steps in tranining.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.18-18
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• 2000

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network are provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shows that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.212-212
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• 2000

This study was performed to construct a hot strip speed measuring system and check over whether the measured speed can be used for improving the mass flow of the head-end part of a hot strip in the 7-stand finishing mill. Because the mass flow in hot rolling mill affects the looper operation and the thickness and width control of a strip, accurate measurement of strip speed ie important. The measured speed was compared with the roll speeds of No. 6 and No.7 stand to check the performance of the system and analyzed to find how to apply the speed. As a result, it is shown that the accuracy of the system is enough, strip thickness error can be reduced by -275∼+200$\mu\textrm{m}$ using the measured speed and the existing FSU model has low accuracy for predicting forward slip rate. A neural network was developed to calculate forward slip rate instead of FSU model. The test result of the neural network shows that the neural network is more accurate than the FSU model.