• Journal of IKEEE
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• v.2 no.2 s.3
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• pp.255-262
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• 1998

The neural network can roughly be classified as the specialized control, indirect control and general schemes. Neural network is adopted for MPPT of solar array. And back propagation algorithm also is used to train neural network controller. We investigate the possibilities of $P_{max}$ control using the neural networks, and then we also examine about operating the solar cell at an optimal voltage comprise of temperature compensated voltage with boost converter. Proposed boost converter of MPPT system is studied by simulation and is implemented by using a microprocessor(80c196kc) which controls duty ratio of the boost converter.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1697-1700
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• 2003

We propose a new technique for real-time controller design of a autonomous cruise mobile robot with three drive wheels. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and a 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 control performance of the proposed controller is illustrated by performing the computer simulation for trajectory tracking of the speed and azimuth of a autonomous cruise mobile robot driven by three independent wheels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.312-318
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• 2003

We propose a new technique for the cruise control system design of a mobile robot with three 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 teaming architecture. It is proposed a learning controller consisting of too 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 three independent wheels.

• Journal of Institute of Control, Robotics and Systems
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• v.1 no.2
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• pp.88-93
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• 1995

This paper presents a study using neural networks in the design of the tracking controller of robotic systems. Our strategy is to put to use the available knowledge about the robot manipulator, such as estimation models, in the contoller design via the computed torque method, and then to add the neural network to control the remaining uncertainty. The neural network used here learns to provide the inverse dynamics of the plant uncertainty, and acts as an inverse controller. In the simulation study, we verify that the proposed neural network controller is robust not only to structured uncertainties, but also to unstructured uncertainties such as friction models.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2020-2023
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• 2003

In this paper, our proposed system uses the regional Gabor wavelet and Neural Network to implement face recognition similar to human face recognition system, because the Gator wavelet expresses visual recognition system of human mathematically and the regional Neural Network is robust to white noise and partial illumination. This system consists of two stages of building database and recognizing face. One is composed by using the supervised learning of Neural Network. At this time, the Neural Network is applied to the upper and the lower part of face images respectively. The Backpropagation algorithm is used to learn Neural Network. Another consists of calibration of slope of face image, measurement of illumination variant using deviation with average face image and similarity comparison using Euclidean distance measure.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.9
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• pp.71-79
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• 1993

This paper proposes a new test pattern generation methodology for combinational logic circuits using neural networks based on a modular structure. The CUT (Circuit Under Test) is described in our gate level hardware description language. By conferring neural database, the CUT is compiled to an ATPG (Automatic Test Pattern Generation) neural network. Each logic gate in CUT is represented as a discrete Hopfield network. Such a neual network is called a gate module in this paper. All the gate modules for a CUT form an ATPG neural network by connecting each module through message passing paths by which the states of modules are transferred to their adjacent modules. A fault is injected by setting the activation values of some neurons at given values and by invalidating connections between some gate modules. A test pattern for an injected fault is obtained when all gate modules in the ATPG neural network are stabilized through evolution and mutual interactions. The proposed methodology is efficient for test generation, known to be NP-complete, through its massive paralelism. Some results on combinational logic circuits confirm the feasibility of the proposed methodology.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.9
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• pp.41-49
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• 1997

In this paper, we describe the algorithm which controls an unknown nonlinear system with disturbance a using multilayer neural network. The multilayer neural network can be used to approximate any continuous function to any desired degree of accuracy. With the former fact, we approximate an unknown nonlinear system by using of multilayer neural netowrk. WE include a disturbance among the modelling error, and the weight-update rule of multilayer neural network is derived to satisfy Laypunov stability. The whole control system constitutes controller using the feedback linearization method. The weight of neural network which is used to implement nonlinear function is updated by the derived update-rule. The proposed control algorithm is verified through computer simulation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.207-212
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• 2004

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.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.19-19
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• 2000

This paper introduces a new scheme for neural network classifier which can describe the shape of patterns in clustered group by using a self-organizing teeming algorithm. The prototype based neural network classifier can not describe the shape of group and it has low classification performance when the data groups are complex. To improve above-mentioned problem, new neural scheme is introduced. This proposed neural network algorithm can be regarded as the extension of self-organizing feature map which can describe The experimental results shows that the proposed algorithm can describe the shape of pattern successfully.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.7
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• pp.1169-1177
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• 1994

The function estimation characteristics of neural networks can be used sensor signal estimation of the nuclear power plants. In case of applying the neural network to the signal estimation of redundant sensors, it is an important problem that the redundant sensor signals used as the input signals of neural network should be validated. In this paper, we simplify the conventional parity space method in order to input the validated signal to the neural network and lso propose the sensor signal validation method, which estimates the reliable sensor output combining the neural network with the simplified parity space method. The acceptability of the proposed process variable estimation method is demonstrated by using the simulation data in safety injection accident of the nuclear power plant.