Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks.
Alain B. TchagangJulio J. ValdésPublished in: ICANN (Workshop) (2019)
Keyphrases
- neural network
- artificial neural networks to predict
- prediction model
- prediction accuracy
- pseudo inverse
- pattern recognition
- energy consumption
- hopfield neural network
- singular value decomposition
- artificial neural networks
- neural networks and support vector machines
- radial basis function network
- neural nets
- short term prediction
- prediction algorithm
- multi layer perceptron
- genetic algorithm
- energy minimization
- bayesian networks
- fuzzy logic
- chaotic time series
- trace norm
- maximum likelihood
- low rank
- feed forward
- web service composition
- bayesian estimation
- self organizing maps
- fault diagnosis
- radial basis function
- markov random field
- posterior distribution
- neural network ensemble
- support vector
- multilayer perceptron