نتایج جستجو برای: self accelerating parameter

تعداد نتایج: 750519  

2007
Ruth Gregory Nemanja Kaloper Robert C. Myers Antonio Padilla

We examine brane induced gravity on codimension-1 branes, a.k.a DGP gravity, as a theory of five-dimensional gravity containing a certain class of fourdimensional branes. From this perspective, the model suffers from a number of pathologies which went unnoticed before. By generalizing the 5D geometry from Minkowski to Schwarzschild, we find that when the bulk mass is large enough, the brane hit...

2005
Utpal Mukhopadhyay Saibal Ray

Dark matter, the major component of the matter content of the Universe, played a significant role at early stages during structure formation. But at present the Universe is dark energy dominated as well as accelerating. Here, the presence of dark matter has been established by including a time-dependent Λ term in the field equations. This model is compatible with the idea of an accelerating Uni...

2007
Wontae Kim Myung Seok Yoon

In a semiclassically quantized two-dimensional cosmological model, it can be shown that the parameter of the equation of state for the accelerating universe can be positive due to the negative energy density and the negative pressure, which is a little different from the conventional wisdom that the parameter is negative with the positivity of the energy density. Furthermore, we show that the f...

2006
Roy Maartens Elisabetta Majerotto

The DGP brane-world model provides a simple alternative to the standard LCDM cosmology, with the same number of parameters. There is no dark energy – the late universe self-accelerates due to an infrared modification of gravity. We compute the joint constraints on the DGP model from supernovae, the cosmic microwave background shift parameter, and the baryon oscillation peak in the SDSS luminous...

Journal: :Neural Networks 1996
Ralf Solomon J. Leo van Hemmen

Standard backpropagation and many procedures derived from it use the steepest-descent method to minimize a cost function. In this paper, we present a new genetic algorithm, dynamic self-adaptation, to accelerate steepest descent as it is used in iterative procedures. The underlying idea is to take the learning rate of the previous step, to increase and decrease it slightly, to evaluate the cost...

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