Genetic Selection of Training Sets for (Not Only) Artificial Neural Networks

2018 ◽  
pp. 194-206 ◽  
Author(s):  
Jakub Nalepa ◽  
Michal Myller ◽  
Szymon Piechaczek ◽  
Krzysztof Hrynczenko ◽  
Michal Kawulok
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Selection ◽  
Artificial Neural ◽  
Selection Of ◽  
Training Sets

scholarly journals Extending Gaussian process emulation using cluster analysis and artificial neural networks to fit big training sets

2018 ◽  
Vol 13 (3) ◽  
pp. 195-208 ◽  
Author(s):  
Wim De Mulder ◽  
Bernhard Rengs ◽  
Geert Molenberghs ◽  
Thomas Fent ◽  
Geert Verbeke
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Artificial Neural Networks ◽  
Gaussian Process ◽  
Gaussian Process Emulation ◽  
Artificial Neural ◽  
Training Sets

scholarly journals Dewpoint Temperature Prediction Using Artificial Neural Networks

2008 ◽  
Vol 47 (6) ◽  
pp. 1757-1769 ◽  
Author(s):  
D. B. Shank ◽  
G. Hoogenboom ◽  
R. W. McClendon
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Temperature ◽  
Heat Waves ◽  
Weather Data ◽  
Temperature Prediction ◽  
Related Data ◽  
Dewpoint Temperature ◽  
Artificial Neural ◽  
Selection Of

Abstract Dewpoint temperature, the temperature at which water vapor in the air will condense into liquid, can be useful in estimating frost, fog, snow, dew, evapotranspiration, and other meteorological variables. The goal of this study was to use artificial neural networks (ANNs) to predict dewpoint temperature from 1 to 12 h ahead using prior weather data as inputs. This study explores using three-layer backpropagation ANNs and weather data combined for three years from 20 locations in Georgia, United States, to develop general models for dewpoint temperature prediction anywhere within Georgia. Specific objectives included the selection of the important weather-related inputs, the setting of ANN parameters, and the selection of the duration of prior input data. An iterative search found that, in addition to dewpoint temperature, important weather-related ANN inputs included relative humidity, solar radiation, air temperature, wind speed, and vapor pressure. Experiments also showed that the best models included 60 nodes in the ANN hidden layer, a ±0.15 initial range for the ANN weights, a 0.35 ANN learning rate, and a duration of prior weather-related data used as inputs ranging from 6 to 30 h based on the lead time. The evaluation of the final models with weather data from 20 separate locations and for a different year showed that the 1-, 4-, 8-, and 12-h predictions had mean absolute errors (MAEs) of 0.550°, 1.234°, 1.799°, and 2.280°C, respectively. These final models predicted dewpoint temperature adequately using previously unseen weather data, including difficult freeze and heat stress extremes. These predictions are useful for decisions in agriculture because dewpoint temperature along with air temperature affects the intensity of freezes and heat waves, which can damage crops, equipment, and structures and can cause injury or death to animals and humans.

Artificial neural networks in the selection of shoe lasts for people with mild diabetes

2019 ◽  
Vol 64 ◽  
pp. 37-45 ◽  
Author(s):  
Chung-Chuan Wang ◽  
Ching-Hu Yang ◽  
Chung-Shing Wang ◽  
Dandan Xu ◽  
Bo-Shin Huang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mild Diabetes ◽  
Artificial Neural ◽  
Selection Of

scholarly journals Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals

Sensors ◽  
2011 ◽  
Vol 11 (2) ◽  
pp. 1721-1743 ◽  
Author(s):  
Birsel Ayrulu-Erdem ◽  
Billur Barshan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Discrete Wavelet ◽  
Daubechies Wavelet ◽  
Motion Classification ◽  
Artificial Neural ◽  
Leg Motion ◽  
Function Selection ◽  
Selection Of

We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWTdecomposition and reconstruction.

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