Deep Learning with Random Neural Networks

  • Erol GelenbeEmail author
  • Yongha Yin
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)


This paper develops multi-layer classifiers and auto-encoders based on the Random Neural Network. Our motivation is to build robust classifiers that can be used in systems applications such as Cloud management for the accurate detection of states that can lead to failures. Using an idea concerning some to soma interactions between natural neuronal cells, we discuss a basic building block constructed of clusters of densely packet cells whose mathematical properties are based on G-Networks and the Random Neural Network. These mathematical properties lead to a transfer function that can be exploited for large arrays of cells. Based on this mathematical structure we build multi-layer networks. In order to evaluate the level of classification accuracy that can be achieved, we test these auto-encoders and classifiers on a widely used standard database of handwritten characters.


Random neural networks Deep learning G-Networks 



We gratefully acknowledge the support of the EC 7th Framework Program PANACEA Project, Grant Agreement No. 610764, to Imperial College London.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Intelligent Systems and Networks Group, Electrical and Electronic Engineering DepartmentImperial CollegeLondonUK

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