• Proceedings of the IEEK Conference
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• 2000.11b
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• pp.345-348
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• 2000

In this paper, We described the procedures to generate an input-output buffer information specification (IBIS) model in digital IC circuits. We gives the method to describe IBIS standard I/O for the characteristics of I/O buffer and to represent its electrical characteristics. The parameters of I/O structure for I/O buffer modelling are also referred, and an IBIS model for CMOS, TTL IC, ROM and RAM constructed amounts about 216. This IBIS model can be used to the simulation of signal integrity of high speed circuits in a PCB level.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.4
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• pp.14-21
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• 2001

To estimate crack growth rate and cycle ratio uniquely, many investigators have developed various kinds of mechanical parameters and theories. But, thes have produced local solution space through single parameter. Neural Networks can perform patten classification using several input and output parameters. Fatigue damage model by neural networks was used to recognize the relation between da/dN/N/N(sub)f, and half-value breadth ratio B/Bo, fractal dimension D(sub)f, and fracture mechanical parameters in 2024-T3 aluminium alloy. Learned neural networks has ability to predict both crack growth rate da/dN and cycly ratio /N/N(sub)f within engineering estimated mean error(5%).

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.341-347
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• 2000

To estimate crack growth rate and cycle ratio uniquely, many investigators have developed various kinds of mechanical parameters and theories. But, these have produced local solution space through single parameter. Neural Networks can perform pattern classification using several input and output parameters. Fatigue damage model by neural networks was used to recognize the relation between da/dN N/Nf, and half-value breadth ratio B/BO0, fractal dimension Df and fracture mechanical parameters in 2024-T3 ability to predict both crack growth rate da/dN and cycle ratio N/Nf within engineering estimated mean error (5%).

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.972-974
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• 1993

This paper presents a new method to automatically design fuzzy logic controller(FLC). The main problems of designing FLC are how to optimally and automatically select the control rules and the parameters of membership function (MF). Cell state space algorithms (CSS), differential competitive learning (DCL) and multialyer neural network are combined in this paper to solve the problems. When the dynamical model of a control process is known. CSS can be used to generate a group of optimal input output pairs(X, Y) used by a controller. The(X, Y) then can be used to determine the FLC rules by DCL and to determine the optimal parameters of MF by DCL and to determine the optimal parameters of MF by multilayer neural network trained by BP algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.162-165
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• 1996

In this paper, we proposed an optimization method of the membership function and the numbers of fuzzy rule base for the stabilization controller of the inverted pendulum system by genetic algorithm(GAs). Conventional methods to these problems need to an expert knowledge or human experience. The proposed genetic algorithm method will tune automatically the input-output membership parameters and will optimize their rule-base.

• Journal of the Korea Society for Simulation
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• v.12 no.4
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• pp.17-24
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• 2003

In this paper, we propose a model that simulates the reflective waveform from underwater objects by means of Doppler effect, highlight and elongation phenomenon. Also, this paper presents a hardware Implementation of simulation model with the input and output parameters. The underwater target simulator consists of transducer, receiver, transmitter and control parts. According to the experimental results of the simulator, it carried out the performances of real target in response to transmission signal.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.645-647
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• 1998

A position control algorithm for a inverted pendulum is studied. The proposed algorithm is based on a fuzzy theory and a steady state genetic algorithm(SSGA). The conventional fuzzy methods need expert's knowledges or human experiences. The SSGA, which is a optimization algorithm, tunes the input-output membership parameters and fuzzy rules automatically. The computer simulation to control a inverted pendulum is presented to illustrate the approaches.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.830-833
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• 1993

In this paper, an optimal tuning Algorithms is presented to automatically improve the performance of fuzzy controller, using the simplified reasoning method and the proposed complex method. The method estimates automatically the optimal values of the parameters of fuzzy controller, according to the change rate and limitation condition of output. The controller is applied to plants with dead time. Then, computer simulations are conducted at step input and the performances are evaluated in the ITAE.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2959-2961
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• 1999

A position control algorithm for a inverted pendulum is studied. The proposed algorithm is based on a fuzzy theory and Generalized Radial Basis Function(GRBF). The conventional fuzzy methods need expert's knowledges or human experiences. The GRBF, which is an optimization algorithm, tunes automatically the input-output membership parameters and fuzzy rules. The simulation is presented to illustrate the approaches.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.40.4-40
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• 2001

This paper addresses the problem of designing robust adaptive output tracking control for a class of MIMO nonlinear systems which have different number of inputs and outputs The stability of the whole closed-loop system is guaranteed in the sense of Lyapunov and uniformly Itimately boundedness of the tracking error vector as well as estimated parameters are shown. In addition, we show that the restrictive assumptions on input gain matrix which is presumed in the past works can be eliminated by using proposed control law.