• Journal of the Korean Institute of Navigation
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• v.22 no.2
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• pp.47-52
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• 1998

Recently , there have been considerable researches about the fusion of fuzzy logic and neural networks. The propose of thise researches is to combine the advantages of both. After the function of approximation using GMDP (Generalized Multi-Denderite Product)neural network for defuzzification operation of fuzzy controller, a new fuzzy-neural network is proposed. Fuzzy membership function of the proposed fuzzy-neural network can be adjusted by learning in order to be adaptive to the variations of a parameter or the external environment. To show the applicability of the proposed fuzzy-nerual network, the proposed model is applied to a speed control o fDC sevo motor. By the hardware implementation, we obtained the desriable results.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.249-256
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control (FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, FLC and ANN controller.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.416-419
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under-parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of loaming through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive loaming mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control(FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.714-716
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• 1995

This Paper presents a novel and systematic approach to a self-learning controller. The proposed controller is built on a neural network consisting of a standard back propagation (BNN) and approxinate reasoning (AR). The fuzzy inference and knowledge representation are carried out by the neural network structure and computing, instead of logic inference. An architecture similar to that used by traditional model reference adaptive control system (MRAC) is employed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.29-34
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• 2001

This paper is devoted to investigating direct adaptive neural control of nonlinear systems with uncertain or unknown dynamic models. In the direct adaptive neural networks control area, theoretical issues of the existing backpropagation-based adaptive neural networks control schemes. The major contribution is proposing the variable index control approach, which is of great significance in the control field, and applying it to derive new stable robust adaptive neural network control schemes. This new schemes possess inherent robustness to system model uncertainty, which is not required to satisfy any matching condition. To demonstrate the feasibility of the proposed leaning algorithms and direct adaptive neural networks control schemes, intensive computer simulations were conducted based on the flexible joint robot systems and functions.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.310-330
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• 2012

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In manufacturing systems, the purpose of scheduling is to minimize the production time and costs, by assigning a production facility when to make, with which staff, and on which equipment. Production scheduling aims to maximize the efficiency of the operation and reduce the costs. In order to find an optimal solution to manufacturing scheduling problems, it attempts to solve complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. Genetic algorithm (GA) is one of the generic population-based metaheuristic optimization algorithms and the best one for finding a satisfactory solution in an acceptable time for the NP-hard scheduling problems. GA is the most popular type of evolutionary algorithm. In this survey paper, we address firstly multiobjective hybrid GA combined with adaptive fuzzy logic controller which gives fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and four crucial issues in the manufacturing scheduling including a mathematical model, GA-based solution method and case study in flexible job-shop scheduling problem (fJSP), automatic guided vehicle (AGV) dispatching models in flexible manufacturing system (FMS) combined with priority-based GA, recent advanced planning and scheduling (APS) models and integrated systems for manufacturing.

• The KIPS Transactions:PartC
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• v.11C no.4
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• pp.429-438
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• 2004

This Paper Presents the control structure for incoming traffic from arbitrary node to Provide admission control in policy-based W network management structure using fuzzy logic control approach. The proposed control structure uses scheme for deciding network resource allocation depending on requirements predefined-policies and network states. The proposed scheme enhances policy adapting methods of existing binary methods, and can use resource of network more effectively to provide adaptive admission control, according to the unpredictable network states for predefined QoS policies. Simulation results show that the proposed controller improves the ratio of packet rejection up to 26%, because it Performs the soft adaption based on the network states instead of accept/reject action in conventional CAC(Connection Admission Controller).

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.499-505
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• 2011

In this paper, we proposed an adaptive fuzzy sliding mode control for nonlinear systems without parameter projection method. By modifying the controller structure, the parameters of the estimated input gain function are guaranteed not being identically zero and it is shown that the control scheme will not cause any implementation problem even if the estimated value of input gain function is zero at any moment during on-line operations. Except for the input gain function which an approximate estimate for its lower bound is needed, the proposed control scheme does not assume a priori the exact values of the bounding parameters. Based on Lyapunov synthesis methods, the overall control system guarantees that the tracking error asymptotically converges to zero and that all signals involved in controller are uniformly bounded. This can be illustrated by the simulation results for an inverted pendulum system.

• Smart Structures and Systems
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• v.19 no.4
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• pp.351-360
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• 2017

Protection of structures against natural hazards such as earthquakes has always been a major concern. Semi-active control combines the reliability of passive control and versatility and adaptability of active control. So it has recently become a preferred control method. This paper proposes an algorithm based on Uniform Deformation Theory to mitigate vulnerable buildings using magneto-rheological (MR) damper. Due to the successful performance of fuzzy logic in control of systems and its simplicity and intrinsically robustness, it is used here to regulate MR dampers. The particle swarm optimization (PSO) algorithm is also used as an adaptive method to develop a fuzzy control algorithm that is able to create uniform inter-story drifts. Results show that the proposed algorithm exhibited a desirable performance in reducing both linear and nonlinear seismic responses of structures. Performance of the presented method is indicated in compare with passive-on and passive-off control algorithms.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.3
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• pp.224-232
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• 2006

The paper proposes the artificial neural network(ANN) sensorless control of induction motor drive with adaptive fuzzy logic controller(AFLC). Also, this paper proposes the speed control of induction motor using AFC and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled AFLC and him controller. And this paper is proposed the results to verify the effectiveness of the AFLC and ANN controller.