Network structure formation in pulsating particle populations

2007 ◽  
Vol 76 (5) ◽  
Author(s):  
Akinori Awazu
Keyword(s):  
Structure Formation ◽  
Network Structure

Processes of Silica Network Structure Formation in Reverse Micellar Systems

Langmuir ◽  
2001 ◽  
Vol 17 (14) ◽  
pp. 4189-4195 ◽  
Author(s):  
Makoto Harada ◽  
Shintaro Itakura ◽  
Akihisa Shioi ◽  
Motonari Adachi
Keyword(s):  
Structure Formation ◽  
Network Structure ◽  
Silica Network ◽  
Micellar Systems ◽  
Reverse Micellar Systems ◽  
Reverse Micellar

Studies on Effects of Initialization on Structure Formationand Generalization of Structural Learning with Forgetting

2004 ◽  
Vol 8 (6) ◽  
pp. 621-626 ◽  
Author(s):  
Hiroshi Shiratsuchi ◽  
◽  
Hiromu Gotanda ◽  
Katsuhiro Inoue ◽  
Kousuke Kumamaru ◽  
...  
Keyword(s):  
Neural Networks ◽  
Structure Formation ◽  
Network Structure ◽  
Center Of Gravity ◽  
Input Pattern ◽  
Structural Learning ◽  
Generalization Ability ◽  
Multilayer Neural Networks ◽  
Pass Through

In this paper, our proposed initialization for multilayer neural networks (NN) applies to the structural learning with forgetting. Initialization consists of two steps: weights of hidden units are initialized so that their hyperplanes pass through the center of gravity of an input pattern set, and weights of output units are initialized to zero. Several simulations were performed to study how the initialization effects the structure formation of the NN. From the simulation result, it was confirmed that the initialization gives better network structure and higher generalization ability.

Network structure formation in thermally-induced gelation of glycinin

1984 ◽  
Vol 32 (2) ◽  
pp. 349-352 ◽  
Author(s):  
Takashi Nakamura ◽  
Shigeru Utsumi ◽  
Tomohiko Mori
Keyword(s):  
Structure Formation ◽  
Network Structure ◽  
Thermally Induced

Effect of Structure on Refractive Index for SiO2-B2O3-Ta2O5-ZrO2-Na2O System Glass

2017 ◽  
Vol 727 ◽  
pp. 265-271 ◽  
Author(s):  
Xiao Dian Xu ◽  
Yan Hang Wang ◽  
Cheng Kui Zu ◽  
Peng Zhou
Keyword(s):  
Refractive Index ◽  
Raman Spectrum ◽  
Structure Formation ◽  
Network Structure ◽  
Strong Preference ◽  
Borosilicate Glasses ◽  
Connection Degree ◽  
The Relationship ◽  
Effect Of Structure

The relationship between structure and refractive index for SiO2-B2O3- Ta2O5-ZrO2-Na2O system glasses was investigated via Raman spectrum and V-block technology. The results showed that refractive index of the borosilicate glasses is mainly influenced by network structure such as planar [BO3] triangle, [BO4] tetrahedron and [SiO4] tetrahedron. Refractive index decreases from 1.629 to 1.616 when B2O3 content increases from 15 mol% to 50 mol%. Na2O component has a strong preference to provide non-bridging oxygen (NBO) atoms, which not only promotes the conversion of [BO3] to [BO4] unit but also depolymerizes the network structure. The refractive index has the highest value, =1.6264, when Na2O content reaches to 28 mol%. Both ZrO2 and Ta2O5 can promote structure formation of borosilicate glasses and make higher connection degree. However, the refractive index increasing with Ta2O5 addition is quicker than that with ZrO2 addition.

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