• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.6
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• pp.1135-1141
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• 2004

In this study, this system makes use of the analog infrared rays sensor and converts the feature of fish outline when sensor is operating with CPU(80C196KC). Then, after signal processing, this feature is classified a special feature and a outline of fish by using the neural network, one of the artificial intelligence scheme. This neural network classifies fish pattern of very simple and short calculation. This has linear activation function and the error back propagation is used as a teaming algorithm. And the neural network is learned in off-line process. Because an adaptation period of neural network is too long when random initial weights are used, off-line teaming is induced to decrease the progress time.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.17-27
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• 2005

In this study, it proposes the neural network system for character recognition and restoration. Proposes system composed by recognition part and restoration part. In the recognition part. it proposes model of effective pattern recognition to improve ART Neural Network's performance by restricting the unnecessary top-down frame generation and transition. Also the location feature extraction algorithm which applies with Hangeul's structural feature can apply the recognition. In the restoration part, it composes model of inputted image's restoration by Hopfield neural network. We make part experiments to check system's performance, respectively. As a result of experiment, we see improve of recognition rate and possibility of restoration.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.460-465
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• 2006

This paper presents a new fuzzy learning rule which considers the Euclidean distances between the input vector and the prototypes of classes. The new fuzzy learning rule is integrated into the supervised IAFC neural network 4. This neural network is stable and plastic. We used iris data to compare the performance of the supervised IAFC neural network 4 with the performances of back propagation neural network and LVQ algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.7
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• pp.610-614
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• 1991

This paper proposes a trajectory constrol fo a robot manipulator by using neural network. The inverse dynamic model of manipuator is learned by neural network. The manipulator is controlled by weight values of the learned neural network. The weight valuese is change with a torque of liner vontroller and a acceleration error. Phsically, the totlal torque for a manipualator is a sum of the liner controller torque and the nerural network controller torque. The proposed control effect is estimated by computer simulation.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.326-328
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• 2000

인공신경회로망 기술은 선형 또는 비선형성 계산 문제를 복잡도에 무관하게 학습에 의해 자동으로 근사한다. 또한 알고리즘이 단순하며 잡음에 강하여 다양한 분야에 적용되고 있다. 반면 대상시스템의 특성이나 조건이 변경되면 계산성능을 보장할 수 없고, 계산의 신뢰성 보장 한계가 모호하기 때문에 제어문제에는 실용화가 어려운 것으로 알려져 있다. 제안 모델은 인공신경회로망의 장점을 유지하면서, 위와 같은 문제점을 해결한다. 시뮬레이션을 통하여 제안 모델은 기존 제어기에 비해 우수한 추종제어성능을 보이는 것으로 밝혀졌다.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.179-184
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• 2006

An intuitive understanding of the dynamic pattern generation in asymmetric networks may be considered an essential component in developing models for the dynamic information processing. It has been reported that the neural network with cyclic connections generates multiple limit cycles. The dynamics of discrete time network with cyclic connections has been investigated intensively. However, the dynamics of a cyclic connection neural network in continuous-time has not been well-known due to the considerable complexity involved in its calculation. In this paper, the dynamic behavior of a continuous-time cyclic connection neural network, in which each neuron is connected only to its nearest neurons with binary synaptic weights of ${\pm}1$, has been investigated. Furthermore, the dynamics and stability of the network have been analyzed using a piece-wise linear approximation.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.800-804
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• 2005

This paper presents a fuzzy learning rule which is the fuzzified version of LVQ(Learning Vector Quantization). This fuzzy learning rule 3 uses fuzzy learning rates. instead of the traditional learning rates. LVQ uses the same learning rate regardless of correctness of classification. But, the new fuzzy learning rule uses the different learning rates depending on whether classification is correct or not. The new fuzzy learning rule is integrated into the improved IAFC(Integrated Adaptive Fuzzy Clustering) neural network. The improved IAFC neural network is both stable and plastic. The iris data set is used to compare the performance of the supervised IAFC neural network 3 with the performance of backprogation neural network. The results show that the supervised IAFC neural network 3 is better than backpropagation neural network.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.7
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• pp.75-84
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• 2003

To reduce the effects of the uncertainties due to the modeling error and aerodynamic coefficients, a nonlinear adaptive control system based on neural networks is proposed . Neural networks parameters are adjusted by using an adaptive law. The sliding mode control scheme is used to compensate for the effect of the approximation error of neural networks. Control parameters and neural networks structures are optimized to obtain better performance by using the genetic algorithm. By introducing the concept of multi-groups of populations, the genetic algorithm is modified so that individuals and groups can be simultaneously evolved . To verify the performance of the pro posed algorithm, the optimized neural networks control system is applied to an aircraft longitudinal dynamics.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.207-210
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• 2000

학습법칙은 신경회로망의 성능을 좌우하는 중요한 요소의 하나이다. Kohonen의 합습법칙등이 개발되어 사용되어 왔으나 Underutilization 문제가 있어 실제 사용사에 문제가 있어 왔다. 본 논문에서 제시하는 학습법칙은 이를 부분적으로 해결하였다. 또한 이 학습법칙을 ART(Adaptive Resonance Theory)-1과 Kohonen의 자기 구조 특징 지도의 장점을 조합한 개선된 IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망에 적용하였고, 성능을 평가하기 위해 가우시안 분포의 데이터와 IRIS 데이터를 각각 사용하여 실험하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.176-179
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• 1997

본 논문은 입/출력 관계가 불명확한 가전제품 제어에 인공신경회로망을 응용하여 지능형 제어기를 구현하는 방법에 관한 것이다. 다층신경회로망을 사용하고 Error Back Propagation 학습방법에 의하여 학습되도록 한다. 제어대상물에서 알 수 있는 정보는 입력값과 이에 대응하는 출력값 뿐이며 입력과 출력에 대한 관계를 수학적으로 모델링하기 어려운 경우이다. 인공신경회로망을 이용한 제어를 위하여 Neural Network Emulator(NNE)와 Neural Network Controller(NNC)가 개발되며 각 신경회로망의 초기하중백터는 제어대상에 오프라인 학습으로 결정하고, 자동조절과정에서 온라인 학습하여 새로운 대상제품 상황에 적응하도록 설계되었다. 제안된 지능형 제어시스템은 PC를 이용하여 실시스템에 적용하여 검토되었다.