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
More filters
Journal Article
Comparison of ANN and Finite Element Model for the Prediction of Ultimate Load of Thin-Walled Steel Perforated Sections in Compression
TL;DR: In this paper, the authors used Artificial Neural Network (ANN) to simulate the process of stub column tests based on specific codes and showed that compared with those of the FEM model, the ultimate load predictions obtained from ANN technique were much closer to those obtained from the physical experiments.
Book ChapterDOI
Method for Supporting Product Development
TL;DR: In this article, the authors proposed a method for an effective solution to NPD-related problems based on the application of some computational intelligence and constraint programming techniques to improve problem solving, and performed experiments show that constraint programming significantly reduces the search space and time needed to obtain results in comparison with the entire search space.
Journal ArticleDOI
Apex Method: A New Scalable Iterative Method for Linear Programming
TL;DR: The apex method as discussed by the authors is based on the predictor-corrector framework and proceeds in two stages: the quest stage calculates a rough initial approximation of the linear programming problem and the target stage refines the initial approximation with a given precision.
Proceedings ArticleDOI
Remarks on a Feedforward Feedback Controller Using an Echo State Network for Controlling Dynamic Systems
TL;DR: In this paper , an echo state network was applied to design a servo control system, where the network input comprised d-step ahead reference and current outputs of the object plant, whereas the network output was combined with a feedback controller output to synthesise the control input of the plant.
Proceedings ArticleDOI
Skin Cancer Detection using Convolutional Neural Network
TL;DR: In this article , the authors used a deep learning network to classify malignancy from melanoma images by using a set of features extracted from the melanoma histology and melanoma lesions.
References
More filters
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.