• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.704-709
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• 1993

In this paper, a method of using a look-up table for a fuzzy logic controller is proposed. A look-up table is designed for a fast inference. An algorithm for an inference is developed with a view to decrease execution time. The performance of the developed fuzzy controller is compared with that of the traditional one.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2393-2398
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• 2003

In this paper, we proposes a new fuzzy inference system for modeling nonlinear systems given input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the kernel-based method. The kernel-based method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated result illustrates the effectiveness of the proposed technique.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.96.3-96
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• 2001

In this paper the self-organized and fuzzy inference used stochastic gradient ascent method is proposed. Fuzzy rule and fuzzy set increase as occasion demands autonomously according to the observation information. And two rules(or two fuzzy sets)becoming to be similar each other as progress of learning are unified. This unification causes the reduction of a number of parameters and learning time. Using fuzzy inference and making a rule with an appropriate state division, our proposed method makes it possible to construct a robust reinforcement learning system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.298-301
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• 1996

This paper describes the structure Identification of nonlinear function using Adaptive Neuro-Fuzzy Inference Technique(ANFIS). Nonlinear mapping relationship between inputs and outputs were modeled by Sugeno-Takaki's Fuzzy Inference Method. Specially, the consequent parts were identified using a series of 1st order equations and the antecedent parts using triangular type membership function or bell type ones. According to learning Rules of ANFIS, adjustable parameters were converged rapidly and accurately.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.3
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• pp.58-67
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• 1992

This paper presents a method on applying Genetric Algorithms(GA), which is a well-know high performance optimizing algorithm, to construct the self-organizing fuzzy logic controller. Fuzzy logic controller considered in this paper utilized Sugeno's hybrid inference method. which has an advantage of simple defuzzification process in the inference engine. Genetic algorithm is used to find the iptimal parameters in the FLC. The proposed approach will be demonstrated using 2 d. o. f robot manipulator to verify its effectiveness.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.231-240
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• 2001

Conventional expert systems has been criticized due to its lack of capability to adapt to the changing decision-making environments. In literature, many methods have been proposed to make expert systems more environment-adaptive by incorporating fuzzy logic and neural networks. The objective of this paper is to propose a new approach to building a self-evolving expert system inference mechanism by integrating fuzzy neural network and fuzzy rule extraction technique. The main recipe of our proposed approach is to fuzzify the training data, train them by a fuzzy neural network, extract a set of fuzzy rules from the trained network, organize a knowledge base, and refine the fuzzy rules by applying a pruning algorithm when the decision-making environments are detected to be changed significantly. To prove the validity, we tested our proposed self-evolving expert systems inference mechanism by using the bankruptcy data, and compared its results with the conventional neural network. Non-parametric statistical analysis of the experimental results showed that our proposed approach is valid significantly.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.43-52
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• 1994

A design method of rule-based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of ``IF....,THEN...', using the theories of optimization theory , linguistic fuzzy implication rules and fuzzy c-means clustering. Three kinds of method for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 2), and modified linear inference (type 3). In order to identify premise structure and parameter of fuzzy implication rules, fuzzy c- means clustering and modified complex method are used respectively and the least sequare method is utilized for the identification of optimum consequence parameters. Time series data for gas furance and those for sewage treatment process are used to evaluate the performance of the proposed rule-based fuzzy modeling. Comparison shows that the proposed method can produce the fuzzy model with higher accuracy than previous other studies.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1330-1333
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• 1993

In this paper, first, the fuzzy Petri net inference mechanism with learning function is proposed by using the extended fuzzy Petri nets. Secondly, a control system with this new inference engine is proposed. This system can do automatically and easily the knowledge acquisition from the operator's empirical data and can also be controller adaptively under the big parameter change.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1270-1273
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• 1993

Fuzzy optimal control is considered. An optimal sequence of controls is sought best satisfying fuzzy constraints on the controls and fuzzy goals on the states (outputs), with a fuzzy system under control Control over a fixed and specified, implicitly specified, fuzzy, and infinite termination time is discussed. For computational efficiency a small number of reference fuzzy staters and controls is to be assumed by which fuzzy controls and stated are approximated. Optimal control policies reference fuzzy states are determined as a fuzzy relation used, via the compositional rule of inference, to derive an optimal control. Since this requires a large number of overlapping reference fuzzy controls and states implying a low computational efficiency, a small number of nonoverlapping reference fuzzy states and controls is assumed, and then interpolative reasoning is used to infer an optimal fuzzy control for a current fuzzy state.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.179-184
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• 1998

In this paper, a new hierarchical fuzzy inference system (HFIS) using structured Takagi-Sugeno type fuzzy inference units(FIUs) is proposed. The proposed HFIS not only solves the rule explosion problem in conventional HFIS, but also overcomes the readability problem caused by the structure where outputs of previous level FIUs are used as input variables directly. Gradient descent algorithm is used for adaptation of fuzzy rules. The ball and beam control is performed in computer simulation to illustrate the performance of the proposed controller.