• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2286-2288
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• 2001

In this paper, a drive system of induction motor with high performance is realized on the viewpoint of the design and experiment, using the DSP (TMS320F240). The speed controller for induction motor drive system is designed on the basis of a neuro-fuzzy network. The neuro-fuzzy controller acts as a feed-forward controller that provides the right control input for the plant and accomplishes error back-propagation algorithm through the network. The proposed network is used to achieve the high speedy calculation of the space vector PWM (Pulse Width Modulation) and to build the neuro-fuzzy control algorithm, for the real-time control. The proposed neuro-fuzzy algorithm on the basis of DSP shows that experimental results have good performance for the precise speed control of an induction motor drive system. It is confirmed that the proposed controller could provide more improved control performance than conventional v/f vector controllers through the experiment.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.1
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• pp.69-73
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• 2003

Neuro-Fuzzy System is to combine merits of fuzzy inference system and neural networks. The neuro-fuzzy system applies a information about given input-output data to fuzzy theories and deals these fuzzy values with neural networks, e.g. first, redefines normalized input-output data as membership functions and then executes thses fuzzy information with backpropagation neural networks. This paper describes an innovative application of the Taguchi method for the determination of these parameters to meet the training speed and accuracy requirements. Results drawn from this research show that the Taguchi method provides an effective means to enhance the performance of the neuro-fuzzy system in terms of the speed for learning and the accuracy for recall.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.305-311
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• 2003

We propose a new technique of adaptive-neuro controller design to implement real-time control of robot manipulator, Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of loaming a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real time control of robot system using DSPs(TMS320C50)

• Journal of the Society of Naval Architects of Korea
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• v.33 no.2
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• pp.44-55
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• 1996

Since the use of computer for the ship structural design around mid 1960``s, specially many researches on the midship section optimum design were carried out from 1980. For a rule-based optimum design case, there has been a problem of handling a discrete design variable such as plate thickness for a practical use. To deal with the discrete design variable problems and to develop an effective new method using artificial neural network for the ship structural design applications, Neuro-Optimizer combing Hopfield Neural Network and other Simulated Annealing is proposed as a new optimization method and then applied to the fundamental skeletal structures and Midship section of Tanker. From the numerical results, it is confirmed that Neuro-Optimizer could be used effectively as a new optimization method for the structural design.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.97-100
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• 2001

CMAC model was proposed by Albus [6] to formulate the processing characteristics of the human cerebellum. Instead of the global weight updating scheme used in the back propagation, CMAC use the local weight updating scheme. Therefore, CMAC have the advantage of fast learning and high convergence rate. In this paper, simulate Color Calibration by CMAC in color images and design hardware by VHDL-base high-level synthesis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.493-496
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• 2000

In this paper, an adaptive neuro-fuzzy controller using the conditional fuzzy c-means(CFCM) methods is proposed. Usually, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Finally, we applied the proposed method to the water path temperature control system and obtained a better performance than previous works.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.395-400
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• 1993

This paper presents an application of back propagation neural network to the tracking control of line of sight stabilization system. We design a neuro-control system having two neural networks one for learning system dynamics and the other for control. We use a learning method which adjusts learning rate and momentem as a function of plant output error and error change.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.845-848
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• 1993

This paper proposes a design method of fuzzy phase-lead compensator and its self-learning by neural network. The main feature of the fuzzy phase-lead compensator is to have parameters for effectively compensating phase characteristics of control systems. An important theorem which is related to phase-lead compensation is derived by introducing concept of frequency characteristics. We propose a design procedure of fuzzy phase-lead compensators for linear controlled objects. Furthermore, we realize a neuro-fuzzy compensator for unknown or nonlinear controlled objects by using Widrow-Hoff learning rule.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.414-422
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• 2002

In this paper, we propose a new method of adaptive neuro-fuzzy control using CFCM(Conditional Fuzzy c-means) clustering and fuzzy equalization method to deal with adaptive control problem. First, in the off-line design, CFCM clustering performs structure identification of adaptive neuro-fuzzy control with the homogeneous properties of the given input and output data. The parameter identification are established by hybrid learning using back-propagation algorithm and RLSE(Recursive Least Square Estimate). In the on-line design, the premise and consequent parameters are tuned to RLSE with forgetting factor due to a characteristic of time variant. Finally, we applied the proposed method to the water temperature control system and obtained better results than previous works such as fuzzy control.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.710-717
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• 1993

In this paper, we focus upon the design and applications of adaptive fuzzy-neuro controllers. An intelligent control system is proposed by exploiting the merits of two paradigms, a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to update the fuzzy control rules on-line with the output error. And, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.