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
Construction of a method for predicting the number of enterobacteria in milk using artifical neural networks
Oleksandra Berhilevych,Victoria Kasianchuk,Ihor Chernetskyi,Anastasia Konieva,Lubov Dimitrijevich,Tatyana Marenkova +5 more
TL;DR: In this article, the authors proposed a method for predicting the number of bacteria from the Enterobacteriaceae family in raw milk at its chilled storage and to estimate the predictive capability of ANN.
Journal ArticleDOI
Stereoscopic video quality measurement with fine-tuning 3D ResNets
Proceedings ArticleDOI
Method for Estimating the Location of A Low-frequency Target in A Shallow Sea Based on A Single Vector Hydrophone
TL;DR: A window signal fusion algorithm is proposed, which combines the EM algorithm to achieve adaptive signal extraction and the RNN has the highest accuracy and stability for this type of location estimation of an acoustic source based on the time-domain signal.
Proceedings ArticleDOI
Remarks on a Feedforward Feedback Controller Using an Echo State Network for Controlling Dynamic Systems
TL;DR: In this article , 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.
Building 2D Model of Compound Eye Vision for Machine Learning
TL;DR: Experimental results showed that the proposed method could effectively and accurately detect the distance and azimuth to objects and was useful for solving navigation issues for autonomous mobile robots on the ground plane.
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.