• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.436-440
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• 1992

As an approach to design the intelligent controller, this paper proposes a new FNN(Fuzzy Neural Network) control method using the hybrid combination of fuzzy logic control and neural network. The proposed FNN controller has two important capabilities, namely, adaptation and learning. These functions are performed by the following process. Firstly, identification of the parameters and estimation of the states for the unknown plant are achieved by the MNN(Model Neural Network) which is continuously trained on-line. And secondly, the learning is performed by FNN controller. The error back propagation algorithm is adopted as a learning technique. The effectiveness of the proposed method will be demonstrated by computer simulation of a two d.o.f. robot manipulator.

• 제어로봇시스템학회논문지
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• 제6권3호
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• pp.256-262
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• 2000

This paper proposes a new sliding mode controller combined with a multi-layer neural network using the error back propagation learning algorithm,, The network acts as a compensator of the conventional sliding mode controller to improve the control performance when initial assumptions of uncertainty bounds of system parameters are violated. The proposed controller can reduce th steady state error of conventional sliding mode controller with the boundary layer technique Computer simulation results show that the proposed method is effective to control dynamic systems with unexpectably large uncertainties.

• 전자공학회논문지B
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• 제32B권12호
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• pp.1670-1679
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• 1995

In this paper, an optimal variable structure controller with a multilayer neural inverse identifier is proposed. A multilayer neural network with error back propagation learning algorithm is used for construction the neural inverse identifier which is an observer of the external disturbances and the parameter variations of the system. The variable structure controller with the multilayer neural inverse identifier not only needs a small part of a priori knowledge of the bounds of external disturbances and parameter variations but also alleviates the chattering magnitude of the control input. Also, an optimal sliding line is designed by the optimal linear regulator technique and an integrator is introduced for solving the reaching phase problem. Computer simulation results show that the proposed approach gives the effective control results by reducing the chattering magnitude of control input.

• Journal of Advanced Marine Engineering and Technology
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• 제28권2호
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• pp.347-353
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• 2004

Neural oscillator can be applied to oscillator systems such as analysis of image information, voice recognition and etc, Conventional learning algorithms(Neural Network or EBPA(Error Back Propagation Algorithm)) are not proper for oscillatory systems with the complicate input patterns because of its too much complex structure. However, these problems can be easily solved by using a synchrony characteristic of neural oscillator with PLL(phase locked loop) function and a simple Hebbian learning rule, Therefore, in this paper, it will introduce an technique for Recognition of the Korean Character using Phase Synchronization Neural Oscillator and will show the result of simulation.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.199-203
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• 2000

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.

• 한국전산유체공학회지
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• 제14권1호
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• pp.53-61
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• 2009

The objective of this research is to design blade planform to reduce high speed impulsive(HSI) noise from a non-lifting helicopter rotor using CFD method and optimization techniques. As for the aero-acoustic analysis, CFD technique for aerodynamic analysis and Kirchhoff's method for the acoustic analysis were used. As for the optimization method, Kriging-based genetic algorithm(GA) model as a high-fidelity optimization method was chosen. Design variables and constraints are determined for arbitrary blade planform. The result shows that the optimized blade planform with high swept-back and taper ratio can reduce HSI noise by suppressing generation of the strong shock wave on blade surface and propagation of the noise to the farfield flow region.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 추계학술대회논문집 - 한국공작기계학회
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• pp.130-134
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• 2000

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.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권4호
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• pp.220-227
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• 2005

This paper presents hybrid artificial intelligent(HAI) controller based on the vector controlled IPMSM drive system. And it is based on artificial technologies that adaptive neural network fuzzy(A-NNF) is to speed control and artificial neural network(ANN) is to speed estimation. The salient feature of this technique is the HAI controller The hybrid action tolerates any inaccuracies in the fuzzy logic assignment rules or in the neural network stationary weights. Speed estimators using feedforward multilayer and artificial neural network(ANN) are compared. The back-propagation algorithm is easy to derived the estimated speed tracks precisely the actual motor speed. This paper presents the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid intelligent control.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.534-536
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• 1998

The helicopter system is non-linear and complex. Futhermore, because of absence of accurate mathematical model, it is difficult accurately to control its attitude. therefore, we propose a PID Neural Networks control technique to control efficiently its elevation angle and azimuth one. The coefficients of PID controller are automatically adjusted by the back-propagation algorithm of a neural network. The simulation results using MATLAB are introduced.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1995년도 가을 학술발표회 논문집
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• pp.171-177
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• 1995

Fracture mechanics does work for concrete, provided that one uses a proper, nonlinear form of fracture mechanics in which a finite nonlinear zone at fracture front is being considered. The fracture process zone is a region ahead of a traction-free crack, and the development of model of fracture process zone is most important to describe fracture phenomena in concrete. This paper is about fracture behavior of concrete cylinder under lateral pressure. Concrete cylinders were made of high strength normal connote, steel fiber reinforced concrete and steel fiber reinforced polymer-impregnated concrete and concrete and the fracture behavior such as cracking propagation and ultimate load are observed. The fracture process zone is modelled by a Dugdale-Barenblatt type model with linear tension-softening curve and are implemented to the boundary element technique for the fracture analyses of the cylinders. The experimental results are compared with analysis results and tension-softening curves for the steel fiber reinforced concrete and steel fiber reinforced polymer-impregnated concrete are obtained by back analyses.