• Kyungpook Mathematical Journal
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• 제60권2호
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• pp.223-238
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• 2020

We study a lightlike hypersurface M of an indefinite nearly trans-Sasakian manifold ${\bar{M}}$ with an (ℓ, m)-type connection such that the structure vector field ζ of ${\bar{M}}$ is tangent to M. In particular, we focus on such lightlike hypersurfaces M for which the structure tensor field F is either recurrent or Lie recurrent, or such that M itself is totally umbilical or screen totally umbilical.

• 대한수학회보
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• 제54권3호
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• pp.1003-1022
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• 2017

The object of study in this paper is generic lightlike submanifolds of an indefinite trans-Sasakian manifold with a quarter-symmetric metric connection. We study the geometry of two types of generic light-like submanifolds, which are called recurrent and Lie recurrent generic lightlike submanifolds, of an indefinite trans-Sasakian manifold with a quarter-symmetric metric connection.

• Korean Journal of Mathematics
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• 제6권2호
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• pp.281-289
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• 1998

On a generalized Riemannian manifold $X_n$, we may impose a particular geometric structure by the basic tensor field $g_{\lambda\mu}$ by means of a particular connection ${\Gamma}{_\lambda}{^\nu}_{\mu}$. For example, Einstein's manifold $X_n$ is based on the Einstein's connection defined by the Einstein's equations. Many recurrent connections have been studied by many geometers, such as Datta and Singel, M. Matsumoto, and E.M. Patterson. The purpose of the present paper is to study some relations between a generalized semisymmetric $g$-recurrent manifold $GSX_n$ and its submanifold. All considerations in this present paper deal with the general case $n{\geq}2$ and all possible classes.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2004년도 학술발표회
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• pp.1285-1289
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• 2004

Elman Discrete Recurrent Neural Networks Model(EDRNNM) was used to be a suitable short-term hydrological forecasting tool yielding a very high degree of flood stage forecasting accuracy at Musung station of Wi-stream one of IHP representative basins in South Korea. A relative new approach method has recurrent feedback nodes and virtual small memory in the structure. EDRNNM was trained by using two algorithms, namely, LMBP and RBP The model parameters, optimal connection weights and biases, were estimated during training procedure. They were applied to evaluate model validation. Sensitivity analysis test was also performed to account for the uncertainty of input nodes information. The sensitivity analysis approach could suggest a reduction of one from five initially chosen input nodes. Because the uncertainty of input nodes information always result in uncertainty in model results, it can help to reduce the uncertainty of EDRNNM application and management in small catchment.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제21권4호
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• pp.338-342
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• 2015

시계열 데이터를 다룰 수 있는 기계학습모델인 회귀 신경망은 되먹임 연결을 허용하기 때문에 앞먹임 신경망에 비해 훨씬 다양한 구조를 가질 수 있다. 본 연구에서는 은닉 뉴런 간의 네트워크 구조에 초점을 맞추어 그것이 회귀 신경망의 정보처리 능력에 미치는 영향을 탐구하고자 한다. 이를 위해 회귀신경망 모델 중 하나인 Echo State Network을 기준으로 하여, 여러 가지 잘 알려진 네트워크 모델에 따라 은닉 뉴런 간 연결을 구성하고 각각의 경우에 시계열 학습 능력과 동역학을 분석하였다. 그 결과, 은닉 뉴런의 네트워크 구조에 따라 모델의 성능이 큰 폭으로 변하는 것이 관찰되었으며, 그러한 현상은 신경망 동역학이 가지는 임계도(criticality)의 변화와 잘 일치했다. 본 연구의 결과는 기존 회귀 신경망 연구에서 주된 관심사였던 신경망 연결 가중치뿐만 아니라 신경망의 연결 구조가 모델의 성능에 중요한 영향을 미친다는 사실을 보여주며, 성능 향상을 위한 중요한 단서가 될 수 있다.

• Korean Journal of Mathematics
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• 제22권1호
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• pp.207-216
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• 2014

The manifold $^*g-ESX_n$ is a generalized n-dimensional Riemannian manifold on which the differential geometric structure is imposed by the unified field tensor $^*g^{{\lambda}{\nu}}$ through the ES-connection which is both Einstein and semi-symmetric. The purpose of the present paper is to study the algebraic geometric structures of 3-dimensional $^*g-ESX_3$. Particularly, in 3-dimensional $^*g-ESX_3$, we derive a new set of powerful recurrence relations in the first class.

• Korean Journal of Mathematics
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• 제24권3호
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• pp.319-330
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• 2016

The manifold $^*g-ESX_n$ is a generalized n-dimensional Riemannian manifold on which the differential geometric structure is imposed by the unied eld tensor $^*g^{{\lambda}{\nu}}$ through the ES-connection which is both Einstein and semi-symmetric. The purpose of the present paper is to study the algebraic geometric structures of 5-dimensional $^*g-ESX_5$. Particularly, in 5-dimensional $^*g-ESX_5$, we derive a new set of powerful recurrence relations in the first class.

• Korean Journal of Mathematics
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• 제18권2호
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• pp.133-139
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• 2010

The manifold $g-ESX_n$ is a generalized n-dimensional Riemannian manifold on which the differential geometric structure is imposed by the unified field tensor $g_{{\lambda}{\mu}}$ through the ES-connection which is both Einstein and semi-symmetric. In this paper, we investigate the properties of the vectors $X_{\lambda}$, $S_{\lambda}$ and $U_{\lambda}$ of $g-ESX_n$, with main emphasis on the derivation of several useful generalized identities involving it.

• 한국정밀공학회지
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• 제13권12호
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• pp.22-29
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• 1996

In this paper the nodes of the multilayer hidden layers have been modified for modeling the nonlinear systems. The structure of nodes in the hidden layers is built with the feedforward, the cross talk and the recurrent connections. The feedforward links are mapping the nonlinear function and the cross talks and the recurent links memorize the dynamics of the system. The cross talks are connected between the modes in the same hidden layers and the recurrent connection has self feedback, and these two connections receive one time delayed input signals. The simplified steam boiler and the analytic multi input multi output nonlinear system which contains process noise have been modeled using this neural networks.