• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.139-148
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• 1995

In this paper an electro-hydraulic servo system is designed by using a fuzzy control algorithm. In order to drive an optimal fuzzy control system, a simulation program for the control system has been developed. By this program the fuzzifier and defuzzifier, a fuzzy inference method, a fuzzy relational matrix, and a fuzzy inference method are investigated. As a result, Larsen inference method, 9*9 fuzzy relational matrix, and center of area defuzzifier are turned out the best as parameters. Finally this method is compared with the conventional PID algotithm, and showed that the fuzzy control performs better than PID algorithm. The fuzzy control performs very well adap- tation against uncertain disturbances.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.5
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• pp.482-494
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• 1992

In this paper, the neuro-fuzzy inferene system for the effective object recognition is studied. The proposed neuro-fuzzy inference system combines learning capability of neural network with inference process of fuzzy theory, and the system executes the fuzzy inference by neural network automatically. The proposed system consists of the antecedence neural network, the consequent neural network, and the fuzzy operational part, For dissolving the ambiguity of recognition due to input variance in the neuro-fuzzy inference system, the antecedence’s fuzzy proposition of the inference rules are automatically produced by error back propagation learining rule. Therefore, when the fuzzy inference is made, the shape of membership functions os adaptively modified according to the variation. The antecedence neural netwerk constructs a separated MNN(Model Classification Neural Network)and LNN(Line segment Classification Neural Networks)for dissolving the degradation of recognition rate. The antecedence neural network can overcome the limitation of boundary decisoion characteristics of nrural network due to the similarity of extracted features. The increased recognition rate is gained by the consequent neural network which is designed to learn inference rules for the effective system output.

• 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 IEEK Conference
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• 1998.06a
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• pp.897-900
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• 1998

Through fuzzy logic controller is very useful to many areas, it is difficult to build up the rule-base by experience and trial-error. So, effective self-tuning fuzzy controller for the position control of ball and beam is designed. In this paper, we developed the neural network control system with fuzzy-neuron which conducts the adjustment process for the parameters to satisfy have nonlinear property of the ball and beam system. The proposed algorithm is based on a fuzzy logic control system using a neural network learinign algorithm which is a back-propagation algorithm. This system learn membership functions with input variables. The purpose of the design is to control the position of the ball along the track by manipulating the angualr position of the serve. As a result, it is concluded that the neural network control system with fuzzy-neuron is more effective than the conventional fuzzy system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.9
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• pp.1319-1329
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• 2012

Globally stabilizing adaptive fuzzy state- and output-feedback controllers for the fully nonaffine pure-feedback nonlinear system are proposed in this paper. By reformulating the original pure-feedback system to a standard normal form with respect to newly defined state variables, the proposed controllers require no backstepping design procedures. Avoiding backstepping makes the controller structure and stability analysis to be considerably simplified. For the global stabilty of the clossed-loop system, the self-structuring fuzzy system whose memebership functions and fuzzy rules are automatically generated and tuned is adopted. The proposed controllers employ only one fuzzy logic system to approximate unknown nonlinear function, which highlights the simplicity of the proposed adaptive fuzzy controller. Moreover, the output-feedback controller of the considered system proposed in this paper have not been dealt with in any literature yet.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.97-101
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• 1997

In recent years, there have been a number of applications of fuzzy logic in fuzzy reasoning system. The main objective of these applications is to approximate a decision making using the fuzzy reasoning system. This paper designs a fuzzy reasoning model for the decision making of registration fee at a private school, implements it applying for linguistic variables and fuzzy rules, and evaluates the practical availability of the model. The system accepts fuzzy rules, the type of membership functions, the domain of fuzzy sets and hedge, and fuzzifies the linguistic variables to generates fuzzy sets. The fuzzy sets generated are combined to constructs a solution fuzzy set. Finally, the system defuzzifies the solution fuzzy set to calculate a scalar value which is used for decision making.

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.89-97
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• 2001

Chen and Chen(FSS, 1993, 159-168) presented a reasonable analytical model of fuzzy closed-loop systems and proposed a method to analyze the stability of fuzzy control by the relational matrix of fuzzy system. Chen, Lu and Chen(IEEE Trans. Syst. Man Cybern., 1995, 881-888) formulated the sufficient and necessary conditions on stability of fuzzy closed-loop control systems. Gang and Chen(FSS, 1996, 27-34) deduced a linguistic relation model of a fuzzy closed loop control system from the linguistic models of the fuzzy controller and the controlled process and discussed the linguistic stability of fuzzy closed loop system by a linguistic relation matrix. In this paper, we study more on their models. Indeed, we prove the existence and uniqueness of equilibrium state $X_e$ in which fuzzy system is stable and give closed form of $X_e$. The same examples in Chen and Chen and Gang and Chen are treated to analyze the stability of fuzzy control systems.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.4
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• pp.11-19
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• 1996

Recently, fuzzy and neural network techniques have been successfully applied to control of complex and ill-defined system in a wide variety of areas, such as robot, water purification, automatic train operation system and automatic container crane operation system, etc. In this paper, we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feedforward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand, feedforward neural networks provide salient features, such as learning and parallelism. In the proposed neuro-fuzzy controller, the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error backpropagation algorithm as a learning rule, while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally, the effectiveness of the proposed controller is verified through computer simulation of an inverted pendulum system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.309-314
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• 2009

This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2308-2310
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• 2001

In the adaptive fuzzy sliding mode control, from a set of a fuzzy IF-THEN rules adaptive fuzzy sliding mode control whose parameters are adjusted on-line according to some adaptation laws is constructed for the purpose of controlling the plant to track a desired trajectory. Most of the research works in nonlinear controller design using fuzzy systems consider the affine system with fixed grid-rule structure based on system state availability. The fixed grid-rule structure makes the order of the controller big unnecessarily, hence the on-line fuzzy rule structure and fuzzy observer based adaptive fuzzy sliding mode controller is proposed to solve system state availability problems. Therefore, adaptive laws of fuzzy parameters for state observer and fuzzy rule structure are established implying whole system stability in the sense of Lyapunov.