• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1949-1954
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• 2005

One of control methods for underactuated manipulators is known as a switching control which selects a partially-stable controller using a prespecified switching rule. A switching computed torque control with a fuzzy energy region method was proposed. In this approach, some partly stable controllers are designed by the computed torque method, and a switching rule is based on fuzzy energy regions. Design parameters related to boundary curves of fuzzy energy regions are optimized offline by a genetic algorithm (GA). In this paper, we discuss on parameters obtained by GA. The effectiveness of the switching fuzzy energy method is demonstrated with some simulations.

• Journal of the Korea Society for Simulation
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• v.9 no.1
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• pp.21-38
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• 2000

A fuzzy dispatching algorithm with adaptable control scheme is proposed for more flexible and adaptable operation of AGV system. The basic idea of the algorithm is prioritization of all move requests based on the fuzzy urgency. The fuzzy urgency is measured by the fuzzy multi-criteria decision-making method, utilizing the relevant information such as incoming and outgoing buffer status, elapsed time of move request, and AGV traveling distance. At every dispatching decision point, the algorithm prioritizes all move requests based on the fuzzy urgency. The performance of the proposed algorithm is compared with several dispatching algorithms in terms of system throughput in a hypothetical job shop environment. Simulation experiments are carried out varying the level of criticality ratio of AGVs , the numbers of AGVs, and the buffer capacities. The rule presented in this study appears to be more effective for dispatching AGVs than the other rules.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.17-20
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• 2002

This paper proposes a new 75 fuzzy model approximation method which reduces error in nonlinear fuzzy model approximation over the V-type decision rules. Employing rational Bezier curves used in computer graphics to represent curves or surfaces, the proposed method approximates the decision rule by constructing a tractable linear equation in the highly non-linear fuzzy rule interval. This algorithm is applied to the self-adjusting air cushion for spinal cord injury patients to automatically distribute the patient's weight evenly and balanced to prevent decubitus. The simulation results indicate that the performance of the proposed method is bettor than that of the conventional TS Fuzzy model in terms of error and stability.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.95-101
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• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.249-252
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• 1996

Fuzzy logic has been successfully used for nonlinear control systems. However, when the plant is complex or expert knowledge is not available, it is difficult to construct the rule bases of fuzzy systems. In this paper, we propose a new method of how to construct automatically the rule bases using fuzzy neural network. Whereas the conventional methods need the training data representing input-output relationship, the proposed algorithm utilizes the gradient of the object function for the construction of fuzzy rules and the tuning of membership functions. Experimental results with the inverted pendulum show the superiority of the proposed method in comparison to the conventional fuzzy controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.275-278
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• 1996

The application of genetic algorithms to fuzzy rule generation holds a great deal of promise in overcoming difficult problems in fuzzy systems design. There are some aspects to be considered when genetic algorithms are used for generating fuzzy rules. In this paper, we will present an aspect about the control surface constructed by the resultant rules. In the extensive simulations, an important observation that the rules searched by genetic algorithms are randomly scattered is made and a solution to this problem is provided by including a smoothness cost in the objective function. We apply the fuzzy rules generated by genetic algorithms to the fuzzy truck backer-upper control system and compare them with the rules made by an expert.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.10
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• pp.2579-2585
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• 1996

In this paper, a complex fuzzy adaptive decision feedback equalizer(CFADFE) based on the LMS algorithm is proposed. The propoed equalizer is based on the complex fuzzy adaptive equalizer. The CFADFE isconstructed from a set of changeable complex fuzzy IF-THEN rules, where the 'IF' part of the rule is characterized by the state from a set of changealble complex fuzzy IF-THEN rules, where the 'IF' part of the rule is characterized by the state of the desision feedback. the role of decision feedback is to reduce the computational complexity. Computer simulation of the decision feedback. The role of decision feedback is to reduce the computational complexity. Computer simulation shosw that the CFADFE notonly reduces the computational complexity but also improves the performance compared with the conventional complex fuzzy adaptive equalizers. We also show that the adaptation speed is greatly improved by incorporating some linguistic information about the channel into the equalzer. It is applied to M-ary QAM digital communication system with linear and nonlinear complex channel characteristics.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1082-1085
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• 1993

In this paper, we notice the fact that a human learning process is characterized by a process under a natural language environment, and discuss an approach of learning based on indirect linguistic instructions. An instruction is interpreted through some meaning elements and each trend. Fuzzy evaluation rule are constructed for the searched meaning elements of the given instruction, and the performance of a system to be learned is improved by the evaluation rules. In this paper, we propose a framework of learning based on indirect linguistic instruction based learning using fuzzy theory: FULLINS(FUzzy-Learning based on Linguistic IN-Struction). The validity of FULLINS is shown by applying it to helicopter flight control.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.183-194
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• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.2
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• pp.9-15
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• 2001

This paper describes a new method of Genetic Algorithms for Adaptive Fuzzy Control System. Previous works using a Multi-population Genetic Algorithm have divided chromosome into two components, rule sets and membership functions. However, in this case bad rule sets disturb optimization in good rule sets and membership functions. A new method for a Multi population Genetic Algorithm suggests three components, good rule sets, bad rule sets, and membership functions. To show the effectiveness of this method, fuzzy controller is applied to a Truck Backing Problem. Results of the computer simulation show good adaptation of the proposed method.