• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.720-729
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• 2005

In this paper, we design the indirect adaptive controller using Wavelet Neural Network(WNN) for unknown nonlinear systems. The proposed indirect adaptive controller using WNN consists of identification model and controller. Here, the WNN is used in both Identification model and controller The WNN has advantage of indicating the location in both time and frequency simultaneously, and has faster convergence than MLPN and RBFN. There are several training methods for WNN, such as GD, GA, DNA, etc. In this paper, we present the Extended Kalman Filter(EKF) based training method. Although it is computationally complex, this algorithm updates parameters consistent with previous data and usually converges in a few iterations. Finally, ore illustrate the effectiveness of our method through computer simulations for the Buffing system and the one-link rigid robot manipulator. From the simulation results, we show that the indirect adaptive controller using the EKF method has better performance than the GD method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.503-509
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• 2014

In this paper, a PID controller with adaptive neural network compensator is proposed to control the formations of mobile robot. The control system is composed of a kinematic controller based on the leader-following robot and dynamic controller for considering the dynamics of the mobile robot. The dynamic controller is constituted by a PID controller and the adaptive neural network compensator for improving the performance and compensating the change in dynamic characteristics. Simulation results show the performance of the PID controller and the neural network compensator for the circular trajectory and linear trajectory. And it is verified that by improving the performance of a PID controller via the adaptive neural network compensator, the following robot's tracking performance is improved.

• 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.

• 전기의세계
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• v.38 no.2
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• pp.31-38
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• 1989

신경회로망의 특성 및 영상인식에서의 사용 가능성 등에 대해 알아보고자 한다. 또한 기존의 통계적 패턴인식 방법과 비교해서 신경회로망의 이점 등을 살펴보고자 하며 영상인식 신경회로망의 핵심적인 연구인 perceptron을 중심으로 학습방법과 응용등에 대해 살펴보고자 한다. 그리고 간단하게 최근 우리나라에서 수행하고 있는 영상 인식 분야의 신경회로망 연구에 대해 살펴보고자 한다.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.241-246
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• 2020

In this paper, we propose a neural network model to converge the marine electric propulsion system and deep learning algorithm to predict the DC/DC converter output current in the electric propulsion regeneration system and to predict the battery charge during regeneration. In order to experiment with the proposed neural network, the input voltage and current of the PCM were measured and the data set was secured on the prototype PCM board. In addition, in order to improve the learning results in the insufficient data set, the scale of the data set was increased through data fitting and its learning was executed further. After learning, the difference between the data prediction result of the neural network model and the actual measurement data was compared. The proposed neural network model effectively showed the prediction of battery charge according to changes in input voltage and current. In addition, by predicting the characteristic change of the analog circuit constituting the DC/DC converter through a neural network, it is determined that the characteristics of the analog circuit should be considered when designing the regeneration system.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.298-300
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• 2003

동영상이나 정지영상에서 사람의 얼굴을 검출 및 인식을 하는 여러 가지 알고리즘이 소개되고 있다. 본 논문에서는 신경망(Neural Network)과 얼굴의 기하학적 특징 중에 하나인 눈과 입을 사용하여 얼굴 영역을 추출하는 방법을 사용한다 신경망은 얼굴 인식을 비롯한 여러 분야에서 쓰이는 좋은 방법 중의 하나 이지만 신경망이 가지고 있는 특성상 많은 오차를 가질 수 있기 때문에 얼굴을 구성하고 있는 요소인 눈과 입을 사용해서 오차를 제거하는 방법을 제안한다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.32-43
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• 2002

In this paper, we propose a pattern classification system for digital signal which is based on neural networks. The proposed system consists of two models of neural network. The first part is a wavelet neural network whose role is a feature extraction. For this part, we compare existing models of wavelet networks and propose a new model for feature extraction. The other part is a wavelet network for pattern classification. We modify the structure of previous wavelet network for pattern classification and propose a learning method. The inputs of the pattern classification wavelet network is connection weights, dilation and translation parameters in hidden nodes of feature extraction network. And the output is a class of the signal which is input of feature extraction network. The proposed system is, applied to classification of EEG signal based on frequency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.40-48
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• 2018

This paper proposes a license-plate recognition algorithm for automobile black-box image which is obtained from the camera moving with the automobile. The algorithm intends to increase the overall recognition-rate of the license-plate by increasing the Korean character recognition-rate using multi-stage neural network for automobile black-box image where there are many movements of the camera and variations of light intensity. The proposed algorithm separately recognizes the vowel and consonant of Korean characters of automobile license-plate. First, the first-stage neural network recognizes the vowels, and the recognized vowels are classified as vertical-vowels('ㅏ','ㅓ') and horizontal-vowels('ㅗ','ㅜ'). Then the consonant is classified by the second-stage neural networks for each vowel group. The simulation for automobile license-plate recognition is performed for the image obtained by a real black-box system, and the simulation results show the proposed algorithm provides the higher recognition-rate than the existing algorithms using a neural network.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.297-300
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• 2001

본 논문에서는 로봇 매니퓰레이터의 제어에 사용할 수 있는 신경망 외란 관측기를 제안하도록 한다. 제안한 신경망 외란 관측기는 다층신경 망의 구조로 신경망 외란관측기의 오차와 제어 오차가 충분히 작은 콤팩트 집합에 절대 상시 유계된다. 본 논문에서 제안하는 신경망 외란 관측기는 기존의 적응 제어기의 단점을 해결한 방식으로 복잡한 회귀 모델을 필요로 하지 않는다. 끝으로 제안한 방식을 3관절 로봇에 적용하여 그 타당성을 확인한다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.5
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• pp.299-307
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• 2003

The local structure of CMAC neural networks (NN) results in better and faster controllers for nonlinear dynamical systems. In this paper, we propose a CMAC NN-based disturbance observer and its corresponding controller for a flexible joint robot. The CMAC NN-based disturbance observer compensates for the parametric uncertainties and the external disturbances throughout the entire mechanical system. Finally, a simulation result is given to demonstrate the effectiveness of proposed design method's robust tracking performance.