• Journal of Advanced Marine Engineering and Technology
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• v.28 no.6
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• pp.1010-1016
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• 2004

In this paper, a scheme for estimating the parameters of solar cells and a NN-based emulator for predicting the maximum power point are presented. The diode model with series and shunt resistors is used to estimate parameters highly affecting its V-I characteristic curve and both a real-coded genetic algorithm and the model adjustment technique are employed. For implementing the emulator, a multi-layered neural network incorporating with the BP algorithm is used. A set of simulation works using both field data and generated data are carried out to demonstrate the effectiveness of the proposed method.

• The Transactions of the Korean Institute of Power Electronics
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• v.2 no.1
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• pp.39-45
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• 1997

In this paper, a high speed motor drive system using an indirect adaptive neural network controller is proposed. In the variable high speed motor drives, the speed response can be deteriorated by long settling time and high overshoot. To obtain a good dynamical performance, an adaptive feedforward controller consisted of Neural Network Controller(NNC) and Neural Network Emulator(NNE) is applied. The NNE is used to identify the parameters and characteristics of high speed motor. To train the controller, the weights are dynamically adjusted using the back propagation algorithm. Computer simulation and implementation of the proposed system is described.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.613-618
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• 1992

We propose a new control architecture which consists of a PI controller and a neural network(NN) controller connected together in parallel. This architecture is well adapted to a wide range of uncertainties and variations of systems. The NN controller is learned through weights of the emulator which identify the dynamic chracteristics of the systems. A performance evaluation level of two NN's decides automatically which controller of the two controllers will be used mainly. The PI controller operates mainly during learning phase of the NN controller whereas a good performance is obtained from the NN controller only, when the NN controller is learned sufficiently.

• Proceedings of the KIPE Conference
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• 1998.11a
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• pp.1-4
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• 1998

In this paper, an auto-tuning method for fuzzy controller based on the neural network is presented. The backpropagated error of neural emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and used for speed control of switched reluctance motor. The experiments are performed to verify the capability of proposed control method on 6/4 salient type SRM. The results show that fuzzy-neural controller is suitable for wide speed range.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.2
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• pp.302-312
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• 1994

In this study, a fuzzy theory is introduced to control the cross-sectional strip shape in cold rolling. A fuzzy controller is developed based on the production data and the operational knowledge. The cold rolled products are characterized into several types based on their irregularities. For each type of irregular strip shape, fuzzy controller calculates the changes of bender forces of work and intermediate rolls using fuzzy control algorithm. To simulate the continuous shape control, fuzzy controller is linked with emulator which is developed using neural network. The developed fuzzy controller and emulator simulate the cold rolling process until the irregularities converge to the tolerable range to produce unifrom cross-sectional strip shape. The results from this simulation are reasonable for various irregular strip shapes.

• 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.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.49-58
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• 2015

A floating type of memristor emulator which acts like the behavior of $TiO_2$ memristor has been developed. Most of existing memristor emulators are grounded type which is built disregarding the connectivity with other memristor or other devices. The developed memristor emulator is a floating type whose output does not need to be grounded. Therefore, the emulator is able to be connected with other devices and be utilized for the interoperability test with various other circuits. To prove the floating function of the proposed memristor emulator, a Wheatstone bridge is built by connecting 4 memristor emulators in series and parallel. Also this bridge circuit suggest that it is possible to weight calculation of the neural network synapse.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.3
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• pp.861-871
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• 1996

In this study, a fuzzy control algorithm was developed for the randomly irregular shape of cold-rolled strip. Currently developed fuzzy control algorithm consists of two parts: the first part calculates the changes of work and intermediate roll bender forces based on the symmetric part of the irregular strip shape, and the second part calculates the weighting factors based on the asymmetric part and modifies the pre-determined roll bender forces according to the weighting factors. As a result of this, bender froces applied at the both sides of the cold-rolled strip were different. In order to simulate the continuous shape control. fuzzy controller developed was linked with emulator which was developed based on neural network. The fuzzy controller and emulator developed simulated the cold rolling process until irregular shape converged to a tolerable range in producing uniform cross-sectional strip shape. The results obtained from the simulation were reasonable for various irregular strip shapes.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.286-290
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• 2000

Optimal neuro-control algorithm is extended to the control of a multi-degree-of-freedom structure. An active mass driver(AMD) system on the top roof is used as an exciter. The control signals are made by a multi-layer perceptron(MLP) which is trained by minimizing a sub-optimal performance index. The performance index is a function of both the output responses and the control signals. Structure having nonlinear hysteretic behavior is also trained and controlled by using proposed control algorithm. In training neuro-controller, emulator neural network is not used. Instead, sensitivity-test data are used. Therefore, only one neural network is used for the control system. Both the time delay effect and the dynamics of hydraulic actuator are included in the simulation. Example shows that optimal neuro-control algorithm can be applicable to the multi-degree of freedom structures.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.492-495
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• 1999

In this paper, a research is focused on implementation of the handwritten Korean-character recognition system using a neural coprocessor for PDA application. The proposed coprocessor is composed of a digital neural network called DMNN and a RISC-based dedicated controller in order to achieve high speed as well as compactness. Two neural networks are used for recognition, one for stroke classification out of extended 11 strokes and the other for grapheme classification. Our experimental result shows that the successful recognition rate of 92.1% over 3,000 characters written by 10 persons can be obtained. Moreover, it can be improved to 95.3% when four candidates are considered. The design verification of tile proposed neural coprocessor is conducted using the ASIC emulator for further hardware implementation.