• Proceedings of the Korea Society for Industrial Systems Conference
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• 2000.11a
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• pp.375-389
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• 2000

Fuzzy logic ignores some information in the reasoning process. Neural networks are powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule- inference. network. And the traditional propagation rule is modified. For the search strategies to find out the belief value of a conclusion in the fuzzy inference network, we conduct a simulation to evaluate the search costs for searching sequentially and searching by means of search priorities.

• Journal of Korea Multimedia Society
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• v.4 no.2
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• pp.189-196
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• 2001

Fuzzy logic ignores some information in the reasoning process. Neural networks are powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule - inference network. and the traditional propagation rule is modified.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1281-1284
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• 1993

By the exponential representation form (EF) for fuzzy logic, any fuzzy value a (in fuzzy valued logic or fuzzy linguistic valued logic) can be represented as Bc, where B is called the truth base and C the confidence exponent. This paper will propose the basic concepts of this form and discuss its interesting properties. By using a different truth base, the exponential form can be used to represent the positive and the negative logic in fuzzy valued logic as well as in fuzzy linguistic valued logic. Some Simple application examples of EF for approximate reasoning are also illustrated in this paper.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1138-1146
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• 1996

Fuzzy logic ignores some informations in the reasoning process. Neural networks are powerful tools for the pattern processing. However, to model human knowledges, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy logical reasoning, we construct fuzzy inference net-work based on the neural logic network, extending the existing rule-inferencing network. And the traditional propagation rule is modified. For the search strategies to find out the belief value of a conclusion in the fuzzy inference network, we conduct a simulation to evaluate the search cost for searching sequentially and searching by means of priorities.

• Journal of Industrial Technology
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• v.16
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• pp.231-237
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• 1996

In this paper, the nonlinear I/O characteristic of fuzzy logic controller is analyzed by using cell concept. Sources of the nonlinearity in a fuzzy logic controller include the fuzzification, the fuzzy reasoning and the defuzzification. A closed form expression for the defuzzified output is derived in case of a fuzzy logic controller with two inputs, triangular memberships, MacVicar-Whelan type linguistic rules, and direct fuzzy reasoning. As a result, it is shown that fuzzy logic controller is a nonlinear controller. Also its nonlinearity is analyzed with respect to the conventional PID control and the sliding mode control.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1171-1174
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• 1993

A promising approach to get the benefits of neural networks and fuzzy logic is to combine them into an integrated system to merge the computational power of neural networks and the representation and reasoning properties of fuzzy logic. In this context, this paper presents a fuzzy neural network which is able to code fuzzy knowledge in the form of it-then rules in its structure. The network also provides an efficient structure not only to code knowledge, but also to support fuzzy reasoning and information processing. A learning scheme is also derived for a class of membership functions.

• Korean Journal of Cognitive Science
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• v.13 no.1
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• pp.13-21
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• 2002

Fuzzy logic ignores some information in the reasoning process. Neural network is powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule-inference network. And the traditional propagation rule is modified. Experiments are performed to compare search costs by sequential searching and searching by priority. The experimental results show that the searching by priority is more efficient than the sequential searching as the size of the fuzzy inference network becomes larder and an the number of searching increases.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.143-159
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• 1998

This paper proposes a new type of fuzzy approximate reasoning method that combines a self organizing feature map and a fuzzy logic. Previous methods considered only input part to determine the number of fuzzy rules, while this paper considers both input and output parts simultaneously. Our approach proved to improve the inference performance. We also developed a new index for avoiding overlearning which guarantees more accurate results. Experimental results showed that our approach surpasses the performance of Takagi & Hayashi (1991) approach.

• Journal of Internet Computing and Services
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• v.2 no.2
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• pp.117-126
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• 2001

This paper presents fuzzy Pr/T nets to represent the fuzzy production rules of a knowledge-based system in the level of first-order predicate logic. The fuzzy Pr/T nets are fuzzy extension of the Pr/T nets. Based on the fuzzy Pr/T net, we propose a fuzzy reasoning algorithm. This algorithm is much closer to human intuition and reasoning than other methods because of using the proper belief functions according to fuzzy concepts in fuzzy production rules.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1024-1026
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• 1996

Sources of nonlinearity In a fuzzy logic controller Include the fuzzification, the fuzzy reasoning and the defuzzification. In this paper, a closed form expression for the defuzzified output is derived in case of a fuzzy logic controller with two Inputs, triangular memberships, MacVicar-Whelan type linguistic rules, and direct fuzzy reasoning. As a result, it is shown that fuzzy logic controller is a nonlinear controller. Also its nonlinearity Is analyzed with respect to the conventional PID control and the sliding mode control.