Journal ArticleDOI
Neural networks
Alberto Prieto,Beatriz Prieto,Eva M. Ortigosa,Eduardo Ros,Francisco J. Pelayo,Julio Ortega,Ignacio Rojas +6 more
TLDR
The development and evolution of different topics related to neural networks is described showing that the field has acquired maturity and consolidation, proven by its competitiveness in solving real-world problems.About:
This article is published in Neurocomputing.The article was published on 2016-11-19. It has received 184 citations till now. The article focuses on the topics: Neural modeling fields & Nervous system network models.read more
Citations
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Journal ArticleDOI
From cognitive and clinical substrates to functional profiles: Disentangling heterogeneity in schizophrenia
Marta Bosia,Margherita Bechi,Francesca Bosinelli,Ernestina Politi,Mariachiara Buonocore,Marco Spangaro,Laura Bianchi,Federica Cocchi,Carmelo Guglielmino,Roberto Cavallaro +9 more
TL;DR: A better understanding of the building blocks of these Profiles would lead to more individualized rehabilitation treatments, and highlight the importance of a more in-depth investigation of cognitive and clinical heterogeneity.
Journal ArticleDOI
Improved artificial neural network based on intelligent optimization algorithm
Yi Xu,Minghui He +1 more
TL;DR: The adapted nodes number of the first hidden layer is selected adaptively based on minimizing mean square error (MSE) and improved genetic algorithm (GA) is proposed to train the learning rate and momentum factor dynamically, which includes multi-point crossover and single point mutation.
Journal ArticleDOI
Bagged neural networks for forecasting Polish (low) inflation
TL;DR: A thick modelling approach is utilized in order to investigate the quality of the out-of-sample short-term headline inflation forecasts generated by a combination of bagged single hidden-layer feed-forward artificial neural networks, which statistically outperforms a panel of benchmarks in a number of cases.
Book ChapterDOI
Machine Learning of Combustion LES Models from Reacting Direct Numerical Simulation
TL;DR: A-priori testing of the DNN-based model highlights the representative power of DNN to accurately reproduce the filtered reaction progress variable source term over a range of scales and various flame regimes as seen in an industrial burner.
Journal ArticleDOI
Discrimination of four class simple limb motor imagery movements for brain–computer interface
TL;DR: It can be seen that EMD features outperform DWT features in discriminating the most challenging tasks involving the right foot and left foot imageries, whose EEG data were derived from the same Cz node of the motor cortex.
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
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The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.