Zobrazeno 1 - 10
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pro vyhledávání: '"E.S. Garcia-Trevino"'
Autor:
Pilar Gomez-Gil, Vicente Alarcon-Aquino, Juan Manuel Ramirez-Cortes, E.S. Garcia-Trevino, E. Juárez-Guerra
Publikováno v:
Journal of Signal Processing Systems. 92:187-211
In this paper, we present a novel neural network able to classify epileptic seizures using electroencephalogram (EEG) signals, called “Multidimensional Radial Wavelons Feed-Forward Wavelet Neural Network” (MRW-FFWNN). The network is part of a cla
Akademický článek
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Publikováno v:
Journal of Cleaner Production. 112:2632-2641
Global warming, caused mainly by increased worldwide emissions of greenhouse gases, is currently one of the greatest threats to the environment and human societies. Mexico has set an ambitious goal of reducing 30% of its greenhouse gases emissions by
Publikováno v:
Biological Invasions. 17:1215-1225
Previous studies have demonstrated that most introduced species go through rapid phenotypic change during their first decades to centuries of being introduced to a new range. However, little is known about the trends these phenotypic changes follow t
Autor:
Israel Cruz-Vega, Pilar Gomez-Gil, Juan Manuel Ramirez-Cortes, E.S. Garcia-Trevino, Jose de Jesus Rangel-Magdaleno
Publikováno v:
I2MTC
This paper presents an assessment of three classification models, all based on computational intelligence techniques, for the automatic identification of three possible conditions found in induction motors: healthy, with a half broken rotor bar or wi
Autor:
E.S. Garcia-Trevino, Javier A. Barria
Publikováno v:
Computational Statistics & Data Analysis. 56:327-344
There has been an important emergence of applications in which data arrives in an online time-varying fashion (e.g. computer network traffic, sensor data, web searches, ATM transactions) and it is not feasible to exchange or to store all the arriving
Publikováno v:
ITSC
This paper proposes a novel inference method to estimate lane-level traffic flow, time occupancy and vehicle inter-arrival time on road segments where local information could not be measured and assessed directly. The main contributions of the propos
Publikováno v:
CONIELECOMP
Wavelet-networks are inspired by both the feed forward neural networks and the theory underlying wavelet decompositions. This special kind of networks has proved its advantages over other networks schemes, particularly in approximation and prediction
Publikováno v:
2006 15th International Conference on Computing.
Wavelet-networks are inspired by both the feed-forward neural networks and the theory underlying wavelet decompositions. This special kind of networks has proved its advantages over other network schemes, particularly in approximation and prediction
Publikováno v:
CONIELECOMP
This paper presents a wavelet neural-network for learning and approximation of chaotic time series. Wavelet-networks are inspired by both feed-forward neural networks and the theory underlying wavelet decompositions. Wavelet networks a class of neura