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

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.2
• /
• pp.265-272
• /
• 2009

Fuzzy number intuitionistic fuzzy sets (FNIFSs), each of which is characterized by a membership function and a non-membership function whose values are trigonometric fuzzy number rather than exact numbers, are a very useful means to describe the decision information in the process of decision making. Wang [10] developed some arithmetic aggregation operators, such as the fuzzy number intuitionistic fuzzy weighted averaging (FIFWA) operator, the fuzzy number intuitionistic fuzzy ordered weighted averaging (FIFOWA) operator and the fuzzy number intuitionistic fuzzy hybrid aggregation (FIFHA) operator. In this paper, based on the FIFHA operator and the FIFWA operator, we investigate the group decision making problems in which all the information provided by the decision-makers is presented as fuzzy number intuitionistic fuzzy decision matrices where each of the elements is characterized by fuzzy number intuitionistic fuzzy numbers, and the information about attribute weights is partially known. An example is used to illustrate the applicability of the proposed approach.

• Journal of Welding and Joining
• /
• v.13 no.1
• /
• pp.156-166
• /
• 1995

Experimental models which are able to determine the deviation between weld line and weaving center by measuring the weld current during welding were proposed for the gas metal arc welding process. The models were used for developing a weld seam tracking system which controls the weaving speed of a welding torch. However, it was revealed that the tracking result of the system is affected by the welding conditions. Thus an arc sensor system was developed by using fuzzy control approach for overcoming the difficulty of modelling the nonlinear process. The rule base and parameters of the fuzzy control system were determined on the basis of the results of experiments. This fuzzy control system has shown the successful tracking capability for the wide operating range of welding conditions.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.8 no.6
• /
• pp.70-77
• /
• 1998

Although many methods of knowledge acquisition has been developed in the exper systems field, such a need form causal knowledge acquisition hs not been stressed relatively. In this respect, this paper is aimed at suggesting a causal knowledge acquisition process, and then investigate the causal knowledge-based inference process. A vehicle for causal knowledge acquisition is FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain. Although FCM has a plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring a more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approach, we prototyped a causal knowledge-driven inference engine named CAKES and then experimented with some illustrative examples.

• Journal of Advanced Navigation Technology
• /
• v.15 no.6
• /
• pp.1098-1103
• /
• 2011

In spite of many advantages of cloud computing, security concerns are a barrier in users' adopting the cloud service. In this paper, we evaluate relative priorities between security attributes of ISO 7498-2 standards affecting overall security quality in cloud computing. For an objective evaluation, the fuzzy AHP(Analytic hierarchical process) is applied. The evaluation results represented the relative priority with concrete number can be an effective management method to choose and develop the cloud computing service.

• Proceedings of the KIEE Conference
• /
• 2005.07d
• /
• pp.2873-2875
• /
• 2005

The optimal structure of the conventional Fuzzy Polynomial Neural Networks (FPNN)[3] depends on experience of designer. For the conventional Fuzzy Polynomial Neural Networks, input variable number, number of input variable, number of Membership Functions(MFs) and consequence structures are selected through the experience of a model designer iteratively. In this paper, we propose the new design methodology to find the optimal structure of Fuzzy Polymomial Neural Network by using Genetic Algorithms(GAs)[4, 5]. In the sequel, It is shown that the proposed Advanced Genetic Algorithms based Fuzzy Polynomial Neural Network(Advanced GAs-based FPNN) is more useful and effective than the existing models for nonlinear process. We used Medical Imaging System(MIS)[6] data to evaluate the performance of the proposed model.

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.7
• /
• pp.876-882
• /
• 1999

It is difficult to determine the feeding rate of coagulant in the water treatment process, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the genetic-fuzzy system was used in determining the feeding rate of the coagulant. The genetic algorithms are excellently robust in complex optimization problems. Since it uses randomized operators and searches for the best chromosome without auxiliary informations from a population consists of codings of parameter set. To apply this algorithms, we made the lookup table and membership function from the actual operation data of the water treatment process. We determined optimum dosages of coagulant(LAS) by the fuzzy operation, and compared it with the feeding rate of the actual operation data.

• 제어로봇시스템학회:학술대회논문집
• /
• 1990.10a
• /
• pp.246-251
• /
• 1990

A fuzzy control scheme has been proposed for a supercritical extraction process which has attracted much attention recently as a new separation technology. Based on the manual operation experience, three control pairs between manipulated and output variables are selected first and then seven membership functions are defined for control error and time rate of the error, respectively for each control pair, resulting in forty nine Fuzzy control rules. In addition to these, the membership functions are defined in two steps (coarse and fine) to enhance control performance. Fuzzy inference is performed using MAX-MTN composition rule and defuzzified control output is calculated based on center of gravity method. The prosed Fuzzy control scheme has been assessed through numerical simulation. As a result, the proposed scheme shows good control performance comparable with that by INA(inverse nyquist array) which usually requires complicated design procedure.

• Proceedings of the KIPE Conference
• /
• 1998.10a
• /
• pp.420-424
• /
• 1998

A new type of controller for DC-DC converters is presented. The proposed neuro-fuzzy controller combines fuzzy logic with neural networks to adjust parameters of the fuzzy controller to the most appropriate. Neither the exact mathematical models of the DC-DC converters nor the tuning process of the parameters of the fuzzy controller are needed in the proposed scheme. Simulation results are presented to show the above process and transient, steady state responses, and load regulation of the given system.

• Journal of the Korea Safety Management & Science
• /
• v.6 no.3
• /
• pp.275-285
• /
• 2004

This paper aims to construct an effective decision making model on selection of product design in product development using fuzzy AHP technique. It is expected that this paper contributes to enhancement of company's market competitiveness by shortening the lead time to develop a new product and minimize initial investment. The proposed model using fuzzy AHP enables quick decision making by integrating and analyzing all customer requirements related to a product. In addition, it can deal with vagueness and uncertainty of decision making process using fuzzy set theory. Decision making processes for evaluating the best selection of product design are also constructed to describe the exact concept of development. A tennis racket is shown as an example. The proposed model is expected to be applied in various fields of managerial decision making processes as well as of product development process.