Increase attractor capacity using an ensembled neural network

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• 作者：Mario Gonzá ; lez ; ^a ; ^b ; ^{mario.gonzalez.rodriguez@udla.edu.ec} ; ^{marsgr6@gmail.com} ; David Dominguez^c ; ^{david.dominguez@uam.es} ; Á ; ngel Sá ; nchez^d ; ^{angel.sanchez@urjc.es} ; Francisco B. Rodrí ; guez^c ; ^{f.rodriguez@uam.es}
• 关键词：Hopfield-type network ; Synaptic dilution ; Network wiring cost ; Storage capacity ; Ensemble of Attractor Neural Networks ; Divide-and-conquer parallelizing
• 刊名：Expert Systems with Applications
• 出版年：2017
• 出版时间：1 April 2017
• 年：2017
• 卷：71
• 期：Complete
• 页码：206-215
• 全文大小：1072 K
• 卷排序：71

文摘

Ensemble of diluted Attractor Neural Networks for pattern retrieval. Increase of network storage capacity by divide-and-conquer approach of subnetworks. Ensemble system triples maximal capacity of the single network with same wiring cost. Engineering application to limited memory systems: embedded systems or smartphones.