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
Application of a Compartmental Spiking Neuron Model with Structural Adaptation for Solving Classification Problems
TL;DR: The conclusion is made about the prospects of using spiking compartmental models of a neuron to increase the bio-plausibility of the implementation of behavioral functions in neuromorphic control systems.
Journal ArticleDOI
N2SkyC: User Friendly and Efficient Neural Network Simulation Fostering Cloud Containers
TL;DR: The new N2SkyC system is presented, a framework for the utilization of Neural Networks as services, aiming for higher flexibility, portability, dynamic orchestration, and performance by fostering microservices and Cloud container technology.
Posted ContentDOI
Investigation of ANN structure on predicting the fracture behavior of additively manufactured Ti-6Al-4V alloys
TL;DR: In this article , a modified Gurson-Tvergaard-needleman model was developed to characterize void growth and void shear mechanism to predict the ductile fracture behavior of SLM-fabricated Ti6Al4V alloys under uniaxial stress states.
Book ChapterDOI
Visualizing Multidimensional Linear Programming Problems
TL;DR: In this article , an n-dimensional mathematical model of the visual representation of a linear programming problem is proposed, which makes it possible to use artificial neural networks to solve multidimensional linear optimization problems, the feasible region of which is a bounded non-empty set.
Advances in Deep Learning through Gradient Amplification and Applications
TL;DR: This research proposes a gradient amplification based approach to train deep neural networks, which improves their training and testing accuraries, addresses vanishing gradients, as well as reduces the training time by reaching higher accuracies even at higher learning rates.
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.
Book
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?
Book
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.