• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.158-165
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• 1999

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses hybrid neuro-genetic technique. Feed-forward multilayer neural networks trained by back-propagation are used to learn the input)the location and dept of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this neural network and genetic algorithm, it is possible to formulate the inverse problem. Neural network training algorithm is the back propagation algorithm with the momentum method to attain stable convergence in the training process and with the adaptive learning rate method to speed up convergence. Finally, genetic algorithm is used to fine the minimum square error.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.3
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• pp.282-289
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• 2005

This paper proposes a fuzzy neural network controller based on the vector control for interior permanent magnet synchronous motor(IPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability This paper does not oかy presents speed control of IPMSM using neuro-fuzzy control(NFC) but also speed estimation using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. Thus, it is presented the theoretical analysis as well as the analysis results to verify the effectiveness of the proposed method in this paper.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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• pp.690-696
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• 2004

This paper is proposed a hybrid intelligent controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neural network-fuzzy(NNF) control and speed estimation using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.7-15
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• 2004

The purpose of this study was to present the method to choose the optimum machining condition for the wire EDM. This was completed by examining the ever-changing quality of the material and by improving the function of the wire electric discharge machine. Precision metal mold products and the unmanned wire electric discharge machining system were used and then applied in industrial fields. This experiment uses the wire electric discharge machine with brass wire electrode of 0.25mm. To measure the precision of the machining surface, average values are obtained from 3 samples of measures of center-line average roughness by using a third dimension gauge and a stylus surface roughness gauge.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.545-550
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• 2004

This paper is proposed a fuzzy neural network controller based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed estimation and control of speed of induction motor using ANN Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• Journal of Ship and Ocean Technology
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• v.4 no.3
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• pp.1-12
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• 2000

A number of numerical methods like Computational Fluid Dynamics(CFD) have been developed to predict the flow fields of a vessel but the present study is developed to infer the wake fields on propeller plane by Statistical Fluid Dynamics(SFD) approach which is emerging as a new technique over a wide range of industrial fields nowadays. Neural network is well known as one prospective representative of the SFD tool and is widely applied even in the engineering fields. Further to its stable and effective system structure, generalization of input training patterns into different classification or categorization in training can offer more systematic treatments of input part and more reliable result. Because neural network has an ability to learn the knowledge through the external information, it is not necessary to use logical programming and it can flexibly handle the incomplete information which is not easy to make a definition clear. Three dimensional stern hull forms and nominal wake values from a model test are structured as processing elements of input and output layer respectively and a neural network is trained by the back-propagation method. The inferred results show similar figures to the experimental wake distribution.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.101-104
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• 2008

In this study, rainfall adjust and forecasting using artificial neural network(ANN) which includes a correlation coefficient is application in Seoul region. It analyzed one-hour rainfall data which has been reported in 25 region in seoul during from 2000 to 2006 at rainfall observatory by AWS. The ANN learning algorithm apply for input data that each region using cross-correlation will use the highest correlation coefficient region. In addition, rainfall adjust analyzed the minimum error based on correlation coefficient and determination coefficient related to the input region. ANN model used back-propagation algorithm for learning algorithm. In case of the back-propagation algorithm, many attempts and efforts are required to find the optimum neural network structure as applied model. This is calculated similar to the observed rainfall that the correlation coefficient was 0.98 in missing rainfall adjust at 10 region. As a result, ANN model has been for suitable for rainfall adjust. It is considered that the result will be more accurate when it includes climate data affecting rainfall.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.2
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• pp.49-57
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• 2010

This paper proposes a Korean document. categorization algorithm using Back Propagation Neural Network(BPNN) with Singular Value Decomposition(SVD). BPNN makes a network through its learning process and classifies documents using the network. The main difficulty in the application of BPNN to document categorization is high dimensionality of the feature space of the input documents. SVD projects the original high dimensional vector into low dimensional vector, makes the important associative relationship between terms and constructs the semantic vector space. The categorization algorithm is tested and compared on HKIB-20000/HKIB-40075 Korean Text Categorization Test Collections. Experimental results show that BPNN algorithm with SVD achieves high effectiveness for Korean document categorization.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1804-1807
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• 1997

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learnign architecture. It is proposed a learning controller consisting of two neural networks-fuzzy based on independent reasoning and a connecton net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.82-87
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• 1997

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.