• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.6
• /
• pp.415-424
• /
• 2004

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.32 no.7
• /
• pp.965-970
• /
• 2003

This study has been performed to develope a method for the simultaneous determination of 1,4-dioxane (DOX), epichlorohydrin (EPC), propylene chlorohydrin (PCH), ethylene chlorohydrin (ECH) and 1,3-dichloro-2-pro-panol (DCP) in polysorbates, chloline chloride, choline bitartrate, modified starch and spices by purge and trapgas chromatography. Experimental design was used to select a suitable trap by measuring the limit of detection (LOD) and to investigate the effect of temperature and salt of extraction, and the percentage of recovery in various matrix. The LOD of DOX, EPC, PCH, ECH and DCP were 1.38$\mu\textrm{g}$, 0.23$\mu\textrm{g}$, 3.30$\mu\textrm{g}$, 3.97$\mu\textrm{g}$, 20.43$\mu\textrm{g}$ respectively, by means of using Vorcarb 3000 trap with 5$0^{\circ}C$ sample sparger. Excluding EPC, the recoveries of target compounds were above 90% in all matrix. Target compounds in polysorbates (17), choline chloride (5), choline bitartrate (5), modified starch (8) and spices (25) were not detected. But 2.5 ppm of DOX was detected in Tween 80.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.6
• /
• pp.533-538
• /
• 2013

The damage caused by Recent frequently occurring locality torrential rains is increasing rapidly. In case of densely populated metropolitan area, casualties and property damage is a serious due to landslides and debris flows and floods. Therefore, the importance of predictions about the torrential is increasing. Precipitation characteristic of the bad weather in Korea is divided into typhoons and torrential rains. This seems to vary depending on the duration and area. Rainfall is difficult to predict because regional precipitation is large volatility and nonlinear. In this paper, Very short-term precipitation forecasting pattern model is implemented using KLAPS data used by Korea Meteorological Administration. we designed very short term precipitation forecasting pattern model using GA-based RBFNNs. the structural and parametric values such as the number of Inputs, polynomial type,number of fcm cluster, and fuzzification coefficient are optimized by GA optimization algorithm.