• Smart Structures and Systems
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• 제30권1호
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• pp.1-15
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• 2022

This article focuses on stability analysis and fuzzy controller synthesis for large neural network (NN) systems consisting of several interconnected subsystems represented by the NN model. Advanced and fuzzy NN differential inclusion (NNDI) for stability based on the developed algorithm with H infinity can be designed based on the evolved biological design. This representation is constructed using sector linearity for NN models. Sector linearity transforms a non-linear model into a linear model based on proposed operations. New sufficient conditions are realized in the form of LMI (linear matrix inequalities) to ensure the asymptotic stability of the trans-Lyapunov function. This transforms the nonlinear model into a linear model based on multiple rules. At last, a numerical case study with simulations is derived as illustration to prove its feasibility in real nonlinear structures.

• Journal of Preventive Medicine and Public Health
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• 제40권5호
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• pp.381-387
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• 2007

Objectives: This study was conducted in order to determine how the association between socioeconomic position(SEP) and health status changes with age among Seoul residents aged 25 and over. Methods: We utilized the 2001 and 2005 Seoul Citizens Health Indicators Surveys. We used self-rated 'poor' health status as an outcome variable, and family income as an indicator of SEP. In order to characterize the differential effects of socioeconomic position on health by age, we conducted separate multivariate analyses by 10-year age groups, controlling for sociodemographic covariates. In order to assess the relative health inequality across socioeconomic groups, we estimated the Relative Index of Inequality (RII). Results: The risk of 'poor health' is significantly high in low family income groups, and this increased risk is seen at all ages. However, the magnitude of relative socioeconomic inequality in health, as measured by the odds ratio and RII, is not identical across age groups. The difference in health across income groups is small in early adulthood (ages 25-34), but increases with age until relatively late in life (ages 35-64). It then decreases among the elderly population (ages more than 65). When the RII reported in 2005 is compared to that reported in 2001, RII can be seen to have increased across all ages, with the exception of individuals aged 25-34. Conclusions: The magnitude of health inequality is the greatest during mid- to late adulthood (ages 45-64). In addition, health inequalities have worsened between 2001 and 2005 across all age groups after economic crisis.

• 한국지능시스템학회논문지
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• 제13권5호
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• pp.582-589
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• 2003

본 논문에서는 퍼지 피드백 선형화 제어 시스템에 대한 강인 안정성 해석과 제어기 설계에 대해서 말하고 있다. 제어 대상인 비선형 시스템을 모델링 하는데 있어서 Takagi-Sugeno (TS) 퍼지 모델 기법을 이용하였고, 이때 발생할 수 있는 모델 불화실성과 외란에 대해 그것의 최대 최소 범위를 안다고 가정하였다. 모델링을 통해서 얻어진 폐구간 시스템에 대한 안정성 판별은 Diagonal Norm based Linear Differential Inclusions (DNLDI) 구조를 이용하여 $L_2$ 강인 안정성 해석을 하였다. 또한, 퍼지 피드백 선형화 제어 시스템을 안정화 시키는 최대 이득을 얻기 위하여 LMI 최적화 계산법을 기반으로한 수치 해석법을 제시하였다. 제안된 방법의 효과를 확인하기 위해서 강인 안정성 해석 및 제어 설계에 대한 간단한 모의실험을 하였다.

• Kyungpook Mathematical Journal
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• 제50권1호
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• pp.37-47
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• 2010

In this present work, the authors obtain Fekete-Szeg$\ddot{o}$ inequality for certain normalized analytic function f(z) defined on the open unit disk for which $\frac{(1-{\alpha})z(D^m_{{\lambda},{\mu}}f(z))'+{\alpha}z(D^{m+1}_{{\lambda},{\mu}}f(z))'}{(1-{\alpha})D^m_{{\lambda},{\mu}}f(z)+{\alpha}D^{m+1}_{{\lambda},{\mu}}f(z)}$ ${\alpha}{\geq}0$) lies in a region starlike with respect to 1 and is symmetric with respect to the real axis. Also certain applications of the main result for a class of functions defined by Hadamard product (or convolution) are given. As a special case of this result, Fekete-Szeg$\ddot{o}$ inequality for a class of functions defined through fractional derivatives is obtained. The motivation of this paper is to generalize the Fekete-Szeg$\ddot{o}$ inequalities obtained by Srivastava et al., Orhan et al. and Shanmugam et al., by making use of the generalized differential operator $D^m_{{\lambda},{\mu}}$.