• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.776-782
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• 2004

Generally, Max-Min CRI (Compositional Rule of Inference ) method by Zadeh and Mamdani is used in the conventional fuzzy inference. However, owing to the problems of Max-Min CRI method, the inference often results in significant error regions specifying the difference between the desired outputs and the inferred outputs. In this paper, I propose a New Max-Min CRI method which can solve some problems of the conventional Max-Min CRI method. And then this method is simulated in a D.C.series motor, which is a bench marking system in control systems, and showed that the new method performs better than the other fuzzy inference methods.

• 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.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2669-2671
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• 2001

In this paper, we propose design a Multi-Fuzzy Inference model structure. In order to determine structure of the proposed Multi-Fuzzy Inference model, HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy are identified by genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy model and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.374-379
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• 2003

본 논문에서는 pattern recognition을 위하여 support vector fuzzy inference system을 제안하였다 Fuzzy inference system의 structure와 parameter를 identification 하기 위하여 Support vector machine을 이용하였으며 에러 최소화 기법으로는 gradient descent 방법을 사용하였다. 제안된 SVFIS 방법의 성능을 파악하고자 COIL 이미지를 이용한 3차원 물체 인식 실험을 수행하였다.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.04a
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• pp.242-250
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• 1999

The objective of this paper is to propose an Extended AND-OR Graph (EAOG)-driven inferential simulation mechanism with which decision makers engaged in electronic commerce (EC) can effectively deal with complicated decision making problem. In the field of traditional expect systems research, AND-OR Graph approach cannot be effectively used in the EC problems in which real-time problem-solving property should be highly required. In this sense, we propose the EAOG inference mechanism for EC problem-solving in which heurisric knowledge necessary for intelligent EC problem-solving can be represented in a form of matrix. The EAOG method possesses the following three characteristics. 1. Realtime inference: The EAOG inference mechanism is suitable for the real-time inference because its computational mechanism is based on matrix computation.2. Matrix operation: All the subjective knowledge is delineated in a matrix form, so that inference process can proceed based on the matrix operation which is computationally efficient.3. Bi-directional inference: Traditional inference method of expert systems is based on either forward chaining or based on either and computational efficiency. However, the proposed EAOG inference mechanism is generically bi-directional without loss of both speed and efficiency.We have proved the validity of our approach with several propositions and an illustrative EC example.

• Journal of Korean Medical classics
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• v.19 no.4
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• pp.202-211
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• 2006

From the study on A Study on A Study on Formation of Oriental Medicine Theory based on Analogical Inference, the conclusion is as follows. Analogical inference belonging to informal logic has very important point that it makes new knowledges of unknown field from basis of common knowledge field. The form of Analogical Inference is "A: a-B: (b)", In "Naegyeong(內經)", models of analogy are classified into two types. One is that of analogical inference making new knowledges in company with effects of Heung-gi(興起), Another is to unite common knowledges, While the example of analogy between military science and medicine belongs to type of the former, example of bureaucracy and medicine belongs to type of the latter, that based on rearrangement of common knowledge. Two type have similar system of national government or military management in basis. In such process of analogical inference, we expect that new knowledges of Oriental Medicine would be accumulated in the future, in the same way of the past.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.141-144
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• 1997

This paper presents a method of navigation sign recognition in indoor environments using a fuzzy inference for an autonomous mobile robot. In order to adapt to image deformation of a navigation sign resulted from variations of view-points and distances, a multi-labeled template matching(MLTM) method and a dynamic area search method(DASM) are proposed. The DASM is proposed to detect correct feature points among incorrect feature points. Finally sugeno-style fuzzy inference are adopted for recognizing the navigation sign.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.747-752
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• 2020

This study examines cognitive impairment, which is one of the results from social exclusion and leads to logical reasoning disorders. This study also investigate how cognitive errors called abductive inference error occur due to cognitive impairment. Present study was performed with 81 college students. Participants were randomly assigned to the group who has experienced social exclusion or to the group who has not experience the social exclusion. We analyzed how the degree of error of abductive inference differs according to the social exclusion experience. The group who has experienced social exclusion showed a higher level of abductive inference error than the group who has not experience. The abductive condition inference value of the group who has experienced social exclusion was higher in the group with the deduction condition inference value of 90% than in the group with the deduction condition inference value of 10%, and the difference was also significant. This study extended the concepts of cognitive impairments, escape theory, cognitive narrowing which are used to explain addiction behavior to human cognitive bias. Also this study confirmed that social exclusion experience increased cognitive impairment and abductive inference error. Future research directions and implications were discussed and suggested.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.675-677
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• 1998

In this paper, we propose the fuzzy inference algorithm with multi-layer structure. MFIS(Multi-layer Fuzzy Inference System) uses PNN(Polynomial Neural networks) structure and the fuzzy inference method. The PNN is the extended structure of the GMDH(Group Method of Data Hendling), and uses several types of polynomials such as linear, quadratic and cubic, as well as the biquadratic polynomial used in the GMDH. In the fuzzy inference method, the simplified and regression polynomial inference methods are used. Here, the regression polynomial inference is based on consequence of fuzzy rules with the polynomial equations such as linear, quadratic and cubic equation. Each node of the MFIS is defined as fuzzy rules and its structure is a kind of neuro-fuzzy structure. We use the training and testing data set to obtain a balance between the approximation and the generalization of process model. Several numerical examples are used to evaluate the performance of the our proposed model.

• Journal of The Korean Association For Science Education
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• v.28 no.3
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• pp.197-210
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• 2008

The purpose of this study is to explore the cases and features of the abductive inference used by young children when trying to explain natural phenomena in everyday life. From observing a 5-year-old's daily activities with his family, and analyzing the data according to the criterion extracted from the form of abductive inference described by C. S. Peirce, a few cases where the child used abductive inferences to explain natural phenomena were found. The abductive inferences in the cases were conducted: (a) based on figural resemblance and behavioral resemblance (b) under the influence by individual belief and communal belief, then (c) resulted in new categorization accompanied by over generalization. Such features of the abductive inference showed the 'double faces'; sometimes encourages and sometimes discourages children's generating better scientific hypotheses and explanations. These results suggest that even young children use abductive inference to explain doubtful natural phenomena in everyday life, although we need to consider carefully with the double aspects of the features of abductive inference for the practical applications to the fields of science education. Finally, several suggestions and following studies for science education are proposed.