• Journal of Agricultural Extension & Community Development
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• v.14 no.2
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• pp.279-299
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• 2007

In order to get the practical suggestions concerning the development of the domestic senior facilities in the rural area, a survey was made in the Philippines and Thailand to grip the policies and programs for senior, by visiting the government institute and senior village facilities. As for the Philippines, Philippine Retirement Authority was attracting investment of foreigners and individuals having Philippine citizenship, by issuing SRRV(the Special Resident Retiree's Visa), and as for Thailand, the Thai Longstay Management Company Limited was providing membership system for the abode of foreigners. In case of the domestic residence for seniors, it is suggested that the development of the senior housing community be designed in accordance with the diversified purposes of residence; that is, the prime concern of the community should be reflected on the characteristics of the retiree group (such as the lifestyle, health situation, etc), and at the same time, special concern also be showed on managing the community club house facilities, and keeping the amicable relations between the retiree residents and the local villagers.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.424-427
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• 2001

In this study, we introduce a concept of neurofuzzy polynomial networks (NFPN), a hybrid modeling architecture combining neurofuzzy networks (NFN) and polynomial neural networks(PNN). NFN contributes to the formation of the premise part of the rule-based structure of the NFPN. The consequence part of the NFPN is designed using PNN. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. We introduce two kinds of NFPN architectures, namely the basic and the modified one. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.812-814
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• 1999

In this paper, we are applied the Genetic Algorithm (GA) to design of fuzzy logic controller (FLC) for a DC Servo-Motor Speed Control. GA is used to design of the membership functions and scaling factor of FLC. To evaluate the performances of the proposed FLC, we make an experiment on FLC for the speed control of an actual DC servo-motor system with nonlinear characteristics. Experimental results show that proposed controller have better performance than those of PD controller.

• 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 Institute of Control, Robotics and Systems
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• v.7 no.6
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• pp.509-517
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• 2001

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require and accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is evaluated through a series of experiments for the various types of inputs while applying disturbances to the hydraulic system. The performance of this controller was compared with those of PID and PD controllers. From these results, We observe be said that the position tracking performance of neuro-fuzzy is better those of PID and PD controllers.

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

• Journal of Korean Society of Water and Wastewater
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• v.18 no.3
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• pp.312-319
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• 2004

The optimum dosage control is presumably the goal of every water treatment plant. However it is difficult to determine the dosage rate of coagulant, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the real number genetic-fuzzy system was used in determining the dosage 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 which consists of codings of parameter set. To apply this algorithms, we made the real number rule table and membership function from the actual operation data of the water treatment plant. We determined optimum dosages of coagulant(LAS) using the fuzzy operation and compared them with the dosage rate of the actual operation data.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.228-228
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• 2000

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require an accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller Parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is obtained through a series of experiments for the various types of input while applying disturbances to the cylinder. .and performance of this controller was compared with that of PID, PD controller. As a experimental result, it can be proven that the position tracking performance of the neuro-fuzzy is better than that of PID and PD controller.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.524-528
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• 1996

Quality information is known to have the characteristic of continuous distribution in many manufacturing processes. It is difficult to describe the process condition by classifying the distribution into discrete ranges which is based on the set concept. Fuzzy control chart has been developed for the control of linguistic data but it still utilizes the dichotomous notion of classical set theory. In this paper, the fuzzy sampling method is studied in order to manage the ambiguous data properly and incorporated for generating fuzzy control chart. The method is based on the fuzzy set concept and considered to be appropriate for the realization of a complete fuzzy control chart. The fuzzy control chart was compared with the conventional generalized p-chart in the sensitivity for quality distribution and robustiness against the noise. The fuzzy control chart with the fuzzy sampling method showed better characteristics.

• Journal of Computational Design and Engineering
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• v.2 no.3
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• pp.137-147
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• 2015

In this study, a new methodology in predicting a system output has been investigated by applying a data mining technique and a hybrid type II fuzzy system in CNC turning operations. The purpose was to generate a supplemental control function under the dynamic machining environment, where unforeseeable changes may occur frequently. Two different types of membership functions were developed for the fuzzy logic systems and also by combining the two types, a hybrid system was generated. Genetic algorithm was used for fuzzy adaptation in the control system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The computational results showed that the hybrid system with a genetic adaptation generated a far better accuracy. The hybrid fuzzy system with genetic algorithm training demonstrated more effective prediction capability and a strong potential for the implementation into existing control functions.