• Kyungpook Mathematical Journal
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• v.60 no.2
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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.

• Bulletin of the Korean Mathematical Society
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• v.54 no.3
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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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• v.6 no.2
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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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.

• KIISE Transactions on Computing Practices
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• v.21 no.4
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• pp.338-342
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• 2015

Recurrent Neural Network (RNN), a machine learning model which can handle time-series data, can possess more varied structures than a feed-forward neural network, since a RNN allows hidden-to-hidden connections. This research focuses on the network structure among hidden neurons, and discusses the information processing capability of RNN. Time-series learning potential and dynamics of RNNs are investigated upon several well-established network structure models. Hidden neuron network structure is found to have significant impact on the performance of a model, and the performance variations are generally correlated with the criticality of the network dynamics. Especially Preferential Attachment Network model showed an interesting behavior. These findings provide clues for performance improvement of the RNN.

• Korean Journal of Mathematics
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• v.22 no.1
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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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• v.24 no.3
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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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• v.18 no.2
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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.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.12
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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.