• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.138-144
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• 1994

An inference network is proposed as a tool for bidirectional approximate reasoning. The inference network can be designed directly from the given fuzzy data(knowledge). If a fuzzy input is given for the inference netwok, then the network renders a reasonable fuzzy output after performing approximate reasoning based on an equality measure. Conversely, due to the bidirectional structure, the network can yield its corresponding reasonable fuzzy input for a given fuzzy output. This property makes it possible to perform forward and backward reasoning in the knowledge base system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1274-1276
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• 1993

We have designed a multiple-valued fuzzy Approximate Analogical-Reseaning system (AARS). The system uses a similarity measure of fuzzy sets and a threshold of similarity ST to determine whether a rule should be fired, with a Modification Function inferred from the Similarity Measure to deduce a consequent. Multiple-valued basic fuzzy blocks are used to construct the system. A description of the system is presented to illustrate the operation of the schema. The results of simulations show that the system can perform about 3.5 x 106 inferences per second. Finally, we compare the system with Yamakawa's chip which is based on the Compositional Rule of Inference (CRI) with Mamdani's implication.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.449-457
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• 2000

The paper describes the study of global approximate optimization utilizing soft computing techniques such as genetic algorithms (GA's), neural networks (NN's), and fuzzy inference systems(FIS). GA's provide the increasing probability of locating a global optimum over the entire design space associated with multimodality and nonlinearity. NN's can be used as a tool for function approximations, a rapid reanalysis model for subsequent use in design optimization. FIS facilitates to handle the quantitative design information under the case where the training data samples are not sufficiently provided or uncertain information is included in design modeling. Properties of soft computing techniques affect the quality of global approximate model. Evolutionary fuzzy modeling (EFM) and adaptive neuro-fuzzy inference system (ANFIS) are briefly introduced for structural optimization problem in this context. The paper presents the success of EFM depends on how optimally the fuzzy membership parameters are selected and how fuzzy rules are generated.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.1
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• pp.16-26
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• 1997

China is one of the few countries where natural disaster strike frequently and cause heavy damage. In this paper, we mathematically develop two models to assess fuzzy risk of natural disaster in China. One is to assess the risk based on database of historical disaster effects by using information diffusion method relevant in fuzzy information analysis. In another model, we give an overview over advanced method to calculate the risk of release, exposure and consequence assessent, where information distribution technique is used to calculate basic fuzzy relationships showing historical experience of natural disasters, and fuzzy approximate inference is employed to study loss risk based on these basic relationships. We also present an examples to show how to use the first model. Result show that the model is effective for natural disaster risk assessment.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1132-1142
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• 2001

The paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Two different approximation methods referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the training data is not sufficiently provided or uncertain information may be included in design process. Fuzzy inference system is the central system for of identifying the input/output relationship in both methods. The paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and presents their generalization capabilities in terms of a number of fuzzy rules and training data with application to a three-bar truss optimization.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.10
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• pp.98-105
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• 1997

In this work, we proesent a bidirectional approximate reasoning method and fuzzy inference network for interval valued decision making systems. For this, we propose a new type of similarity measure between two fuzzy vectors based on the Ordered Weighted Averaging (OWA) operator. Since the proposed similarity measure has a structure to give the extreme values by choosing a suitable weighting vector of the OWA operator, it can render an interval valued similarity value. From this property, we derive a bidirectional approximate reasoning method based on the similarity measure and show its fuzzy inference network implementation for the decision making systems requiring the interval valued decisions.

• 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 the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3689-3696
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• 2011

In decision support system, knowledge workers require aggregation operations of the large data and are more interested in the trend analysis rather than in the punctual analysis. Therefore, it is necessary to provide fast approximate answers rather than exact answers, and to research approximate query answering techniques. In this paper, we propose a new approximation query answering method which is based on Fuzzy C-means clustering (FCM) method and Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed method using FCM-ANFIS can compute aggregate queries without accessing massive multidimensional data cube by producing the KR model of multidimensional data cube. In our experiments, we show that our method using the KR model outperforms the NMF method.

• Journal of the Korean Society of Industry Convergence
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• v.9 no.3
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• pp.173-178
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• 2006

For the design of multivariable fuzzy control systems decomposition of control rules is a efficent inference method since it alleviates the complexity of the problem. In some systems, however, inference error of the Gupta's decomposition method is inevitable because of its approximate nature. In this paper we define indices of applicability which decides whether the decomposition method can be applied to a multivariable fuzzy system or not.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1141-1144
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• 1993

For the design of multivariable fuzzy control systems the decomposition of control rules is preferable since it alleviates the complexity of the problem. In some systems, however, inference error of the Gupta's decomposition method is inevitable because of its approximate nature. In this paper, we propose a new multivariable fuzzy controller with a coordinator which can reduce the inference error of the decomposition method by using an index of applicability.