Parallel processing of chemical information in a local area network—II. A parallel cross-validation procedure for artificial neural networks

1996 ◽  
Vol 20 (4) ◽  
pp. 439-448 ◽  
Author(s):  
E.P.P.A Derks ◽  
M.L.M. Beckers ◽  
W.J. Melssen ◽  
L.M.C. Buydens
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Parallel Processing ◽  
Cross Validation ◽  
Local Area Network ◽  
Local Area ◽  
Chemical Information ◽  
Area Network ◽  
Validation Procedure ◽  
Artificial Neural

scholarly journals Quantum enhanced cross-validation for near-optimal neural networks architecture selection

2018 ◽  
Vol 16 (08) ◽  
pp. 1840005 ◽  
Author(s):  
Priscila G. M. dos Santos ◽  
Rodrigo S. Sousa ◽  
Ismael C. S. Araujo ◽  
Adenilton J. da Silva
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Experimental Analysis ◽  
Cross Validation ◽  
Quantum Memory ◽  
Classical Algorithm ◽  
Artificial Neural ◽  
Quantum Speedup

This paper proposes a quantum-classical algorithm to evaluate and select classical artificial neural networks architectures. The proposed algorithm is based on a probabilistic quantum memory (PQM) and the possibility to train artificial neural networks (ANN) in superposition. We obtain an exponential quantum speedup in the evaluation of neural networks. We also verify experimentally through a reduced experimental analysis that the proposed algorithm can be used to select near-optimal neural networks.

scholarly journals Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation

2019 ◽  
Vol 9 (17) ◽  
pp. 3502 ◽  
Author(s):  
Nicola Baldo ◽  
Evangelos Manthos ◽  
Matteo Miani
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cross Validation ◽  
Laboratory Data ◽  
Stiffness Modulus ◽  
Artificial Neural ◽  
Marshall Stability ◽  
Mean Square Errors ◽  
Fold Cross Validation

The present paper discusses the analysis and modeling of laboratory data regarding the mechanical characterization of hot mix asphalt (HMA) mixtures for road pavements, by means of artificial neural networks (ANNs). The HMAs investigated were produced using aggregate and bitumen of different types. Stiffness modulus (ITSM) and Marshall stability (MS) and quotient (MQ) were assumed as mechanical parameters to analyze and predict. The ANN modeling approach was characterized by multiple layers, the k-fold cross validation (CV) method, and the positive linear transfer function. The effectiveness of such an approach was verified in terms of the coefficients of correlation ( R ) and mean square errors; in particular, R values were within the range 0.965 – 0.919 in the training phase and 0.881 – 0.834 in the CV testing phase, depending on the predicted parameters.

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