Applied Artificial Neural Networks

Edited by
November 2016
258 pages
  • ISBN978-3-03842-270-9 (Hardback)
  • ISBN978-3-03842-271-6 (PDF)

This book is a reprint of the Special Issue Applied Artificial Neural Network that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Environmental & Earth Sciences
Physical Sciences
  • Hardback
© 2014 MDPI; under CC BY-NC-ND license
fuzzy cognitive map (FCM); multi-relational data mining (MRDM); nonlinear Hebbian learning (NHL); real code genetic algorithm (RCGA); complex system; day-ahead; load forecast; artificial neural network; activation function; training process; multi-variate auto-regressive model; 1,1,1,2,3,3,3-heptafluoropropane; R227ea; Song and Mason equation; machine learning; support vector machine; artificial neural networks; nimonic 80A; flow stress; arrhenius-type constitutive model; BP neural network; reservoir sedimentation; artificial neural network (ANN); back propagation (BP); the Three Gorges Reservoir (TGR); sediment flushing; multi-instance multi-label; extreme learning machine; genetic algorithm; armor stones; artificial neural network; harmony search algorithm; rubble mound structure; stability number; magnetic resonance imaging; parameter estimation; support vector machine; dual-tree complex wavelet transform; twin support vector machine; variance; entropy; human–computer interaction; gaze tracking; gaze estimation; pupil-glint vector; direct least squares regression; improved artificial neural network; ecosystem assessment; neural network ensemble; Markov analysis; two-class imbalance problem; average samples; over-sampling; under-sampling; dynamic sampling; carbon fiber fabrics; classification; machine learning; artificial neural networks; support vector machine