• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.561-570
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• 2004

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms(GAs) based Genetically optimized Neurofuzzy Networks(GoNFN) are introduced, and a series of numeric experiments are carried out. The proposed GoNFN is based on the rule-based Neurofuzzy Networks(NFN) with the extended structure of the premise and the consequence parts of fuzzy rules being formed within the networks. The premise part of the fuzzy rules are designed by using space partitioning in terms of fuzzy sets defined in individual variables. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and quadratic are taken into consideration. The structure and parameters of the proposed GoNFN are optimized by GAs. GAs being a global optimization technique determines optimal parameters in a vast search space. But it cannot effectively avoid a large amount of time-consuming iteration because GAs finds optimal parameters by using a given space. To alleviate the problems, the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. In a nutshell, the objective of this study is to develop a general design methodology o GAs-based GoNFN modeling, come up a logic-based structure of such model and propose a comprehensive evolutionary development environment in which the optimization of the model can be efficiently carried out both at the structural as well as parametric level for overall optimization by utilizing the separate or consecutive tuning technology. To evaluate the performance of the proposed GoNFN, the models are experimented with the use of several representative numerical examples.

• Journal of Korea Water Resources Association
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• v.50 no.4
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• pp.253-261
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• 2017

A lot of nonstationary frequency analyses have been studied in recent years as the nonstationarity occurs in hydrologic time series data. In nonstationary frequency analysis, various forms of probability distributions have been proposed to consider the time-dependent statistical characteristics of nonstationary data, and various methods for parameter estimation also have been studied. In this study, we aim to introduce a parameter estimation method for nonstationary Gumbel distribution using ensemble empirical mode decomposition (EEMD); and to compare the results with the method of maximum likelihood. Annual maximum rainfall data with a trend observed by Korea Meteorological Administration (KMA) was applied. As a result, both EEMD and the method of maximum likelihood selected an appropriate nonstationary Gumbel distribution for linear trend data, while the EEMD selected more appropriate nonstationary Gumbel distribution than the method of maximum likelihood for quadratic trend data.

• Journal of agriculture & life science
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• v.45 no.4
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• pp.65-72
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• 2011

The objective of this research is to develop biomass allometric equation for Pinus densiflora in central region and Quercus variabilis. To develop the biomass allometric equation by species and tree component, data for Pinus densiflora in central region is collected to 30 plots (70 trees) and for Quercus variabilis is collected to 15 plots (32 trees). This study is used two independent values; (1) one based on diameter beast height, (2) the other, diameter beast height and height. And the equation forms were divided into exponential, logarithmic, and quadratic functions. The validation of biomass allometric equations were fitness index, standard error of estimate, and bias. From these methods, the most appropriate equations in estimating total tree biomass for each species are as follows: $W=aD^b$, $W=aD^bH^c$; fitness index were 0.937, 0.943 for Pinus densiflora in central region stands, and $W=a+bD+cD^2$, $W=aD^bH^c$; fitness index were 0.865, 0.874 for Quercus variabilis stands. in addition, the best performance of biomass allometric equation for Pinus densiflora in central region is $W=aD^b$, and Quercus variabilis is $W=a+bD+cD^2$. The results of this study could be useful to overcome the disadvantage of existing the biomass allometric equation and calculate reliable carbon stocks for Pinus densiflora in central region and Quercus variabilis in Korea.