global positioning system (gps) signals provide valuable information about ionosphere physical structure. using these signals, can be derived total electron content (tec) for each line of sight between the receiver and the satellite. for historic and other sparse data sets, the reconstruction of tec images is often performed using multivariate interpolation techniques. recently it has become cl...

Nonlinear function approximation is one of the most important tasks in system analysis and identification. Several models have been presented to achieve an accurate approximation on nonlinear mathematics functions. However, the majority of the models are specific to certain problems and systems. In this paper, an evolutionary-based wavelet neural network model is proposed for structure definiti...

Geometric Dilution of Precision (GDOP) is a coefficient for constellations of Global Positioning System (GPS) satellites. These satellites are organized geometrically. Traditionally, GPS GDOP computation is based on the inversion matrix with complicated measurement equations. A new strategy for calculation of GPS GDOP is construction of time series problem; it employs machine learning and artif...

Photometric data-driven classification of supernovae becomes a challenge due to the appearance real-time processing big data in astronomy. Recent studies have demonstrated superior quality solutions based on various machine learning models. These models learn classify supernova types using their light curves as inputs. Preprocessing these is crucial step that significantly affects final quality...

Tight bounds on the approximation rates of nonlinear approximation by variable-basis functions, which include feedforward neural networks, are investigated. The connections with recent results on neural network approximation are discussed.

The universality of a quantum neural network refers to its ability approximate arbitrary functions and is theoretical guarantee for effectiveness. A non-universal could fail in completing the machine learning task. One proposal encode data into identical copies tensor product, but this will substantially increase system size circuit complexity. To address problem, we propose simple design dupli...