Generating weights and generating vectors to map complex functions with artificial neural networks

2008 ◽  
Author(s):  
R. Neville ◽  
S. Holland
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Complex Functions ◽  
Artificial Neural

scholarly journals Artificial Neural Networks in the Prediction of Genetic Merit to Flowering Traits in Bean Cultivars

Agriculture ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 638
Author(s):  
Renato Domiciano Silva Rosado ◽  
Cosme Damião Cruz ◽  
Leiri Daiane Barili ◽  
José Eustáquio de Souza Carneiro ◽  
Pedro Crescêncio Souza Carneiro ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Breeding Value ◽  
Best Linear Unbiased Predictor ◽  
Genetic Merit ◽  
Complex Functions ◽  
Additive Gene ◽  
Best Linear Unbiased ◽  
Artificial Neural ◽  
The Individual

Flowering is an important agronomic trait that presents non-additive gene action. Genome-enabled prediction allow incorporating molecular information into the prediction of individual genetic merit. Artificial neural networks (ANN) recognize patterns of data and represent an alternative as a universal approximation of complex functions. In a Genomic Selection (GS) context, the ANN allows automatically to capture complicated factors such as epistasis and dominance. The objectives of this study were to predict the individual genetic merits of the traits associated with the flowering time in the common bean using the ANN approach, and to compare the predictive abilities obtained for ANN and Ridge Regression Best Linear Unbiased Predictor (RR-BLUP). We used a set of 80 bean cultivars and genotyping was performed with a set of 384 SNPs. The higher accuracy of the selective process of phenotypic values based on ANN output values resulted in a greater efficacy of the genomic estimated breeding value (GEBV). Through the root mean square error computational intelligence approaches via ANN, GEBV were shown to have greater efficacy than GS via RR-BLUP.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural
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