• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.301-309
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• 2005

Learning program's behavior using machine learning techniques based on system call audit data is an effective intrusion detection method. Rule teaming, neural network, statistical technique, and hidden Markov model are representative methods for intrusion detection. Among them neural networks are known for its good performance in teaming system call sequences. In order to apply it to real world problems successfully, it is important to determine their structure. However, finding appropriate structure requires very long time because there are no formal solutions for determining the structure of networks. In this paper, a novel intrusion detection technique using evolutionary neural networks is proposed. Evolutionary neural networks have the advantage that superior neural networks can be obtained in shorter time than the conventional neural networks because it leams the structure and weights of neural network simultaneously Experimental results against 1999 DARPA IDEVAL data confirm that evolutionary neural networks are effective for intrusion detection.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.473-475
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• 1992

This paper compares performance of neural and fuzzy truck backer-upper control systems. Conventional controllers require a mathematical model of how outputs depend on inputs. Neural and fuzzy control systems offer a key advantage over conventional control systems. They are model-free controllers. Neural networks learn a control process by examples (training samples). Fuzzy systems directly encode designer's experience as IF-THEN rules. For robustness test, we gradually removed training samples for the neural controller, and fuzzy rules for the fuzzy system. The errors increased laster in the neural controller than in the fuzzy system.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.41-48
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• 2003

This paper presents an indirect adaptive neuro controller using modified chaotic neural networks(MCNN) for nonlinear dynamic system. A modified chaotic neural networks model is presented for simplifying the traditional chaotic neural networks and enforcing dynamic characteristics. A new Dynamic Backpropagation learning method is also developed. The proposed MCNN paradigm is applied to the system identification of a MIMO system and the indirect adaptive neuro controller. The simulation results show good performances, since the MCNN has robust adaptability to nonlinear dynamic system.

• Asia pacific journal of information systems
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• v.11 no.4
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• pp.99-111
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• 2001

The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.9-14
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• 2022

In the IoT(internet of things) where various devices can be connected, failure of essential devices may lead to a lot of economic and life losses. For reducing the losses, fault diagnosis techniques have been considered an essential part of IoT. In this paper, the method based on a graph neural network is proposed for determining fault and classifying types by extracting features from vibration data of systems. For training of the deep learning model, fault dataset are used as input data obtained from the CWRU(case western reserve university). To validate the classification performance of the proposed model, a conventional CNN(convolutional neural networks)-based fault classification model is compared with the proposed model. From the simulation results, it was confirmed that the classification performance of the proposed model outweighed the conventional model by up to 5% in the unevenly distributed data. The classification runtime can be improved by lightweight the proposed model in future works.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.153-166
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• 2003

This study presents the time-divided forecasting model to integrate evolutionary optimization algorithm and change point detection based on artificial neural networks (ANN) for the prediction of (Korea) stock price index. The genetic algorithm(GA) is introduced as an evolutionary optimization method in this study. The basic concept of the proposed model is to obtain intervals divided by change points, to identify them as optimal or near-optimal change point groups, and to use them in the forecasting of the stock price index. The proposed model consists of three phases. The first phase detects successive change points. The second phase detects the change-point groups with the GA. Finally, the third phase forecasts the output with ANN using the GA. This study examines the predictability of the proposed model for the prediction of stock price index.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.579-582
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• 1999

In this paper, we propose a human face detection algorithm using adaptive skin color model and neural networks. To attain robustness in the changes of illumination and variability of human skin color, we perform a color segmentation of input image by thresholding adaptively in modified hue-saturation color space (TSV). In order to distinguish faces from other segmented objects, we calculate invariant moments for each face candidate and use the multilayer perceptron neural network of backpropagation algorithm. The simulation results show superior performance for a variety of poses and relatively complex backgrounds, when compared to other existing algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1175-1176
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• 1993

This paper introduces a new model for forecasting groundwater level on the basis of analysing defect of finite element method. The new model is built with fuzzy sets and neural networks. It is convenient for use. We computed the groundwater level of one city in P. R. China with it and got a very satisfactory result. It can be popularized to corecast groundwater level of mine.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.233-242
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• 1995

In this paper, we introduce the identification model of dynamic system using the neural networks, We propose two identification models. The output of the parallel identification model is a linear combination of its past values as well as those of the input. The series-parallel model is a linear combination of the past values in the input and output of the plant. To generate stable adaptive laws, we prove that the series-parallel model is found to be proferable.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1561-1564
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• 1997

In the study, simulation result was studied by connecting PID controller in series to the established Neural Networks Controller. Neural Network model is composed of two layers to evaluate tracking performance improvement. The reqular dynamic characteristics was also studied for the expected error to be minimized by using Widrow-Hoff delta rule. As a result of the study, We identified that tracking performance inprovement was developed more in case of connecting PID than Neural Network Contoller and that tracking plant parameter in 251 sample was approached rapidly case of time variable.