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Deep Learning Cluster Structures for Management Decisions: The Digital CEO.

Will Serrano1

  • 1Intelligent Systems and Networks Group; Imperial College London, London SW7 2AZ, UK. g.serrano11@imperial.ac.uk.

Sensors (Basel, Switzerland)
|October 6, 2018
PubMed
Summary
This summary is machine-generated.

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This study introduces a Deep Learning Cluster Structure that mimics brain decision-making for network routing. It combines reinforcement learning and deep learning for efficient, brain-like packet routing decisions in networks.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background:

  • Traditional network routing struggles with complex, dynamic environments.
  • Emulating biological learning mechanisms offers potential for more adaptive network management.

Purpose of the Study:

  • To present a novel Deep Learning (DL) Cluster Structure for management decisions in networks.
  • To emulate brain-like information processing and decision-making for packet routing.

Main Methods:

  • The proposed model integrates Random Neural Network (RNN) Reinforcement Learning for local decisions and Deep Learning for memory.
  • The DL Cluster Structure was applied to Cognitive Packet Network (CPN) routing, utilizing Quality of Service (QoS) and Cyber Security metrics.
  • A management layer of DL clusters (QoS, Cyber, CEO) made final routing decisions.
Keywords:
cognitive packet networkcybersecuritydeep learning clustersquality of servicerandom neural networkrouting

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Main Results:

  • The DL Cluster Structure demonstrated promising performance in simulations across various network sizes and scenarios.
  • The model effectively integrated QoS and security metrics for adaptive routing.
  • The system showed an ability to learn and make informed packet routing decisions.

Conclusions:

  • The developed DL Cluster management structure represents a significant step towards brain-emulating network systems.
  • This approach offers a new mechanism for intelligent packet transmission, learning, and routing.
  • The findings suggest a viable pathway for creating more autonomous and adaptive communication networks.