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A theoretical framework for parallel implementation of deep higher order neural networks


Xu, S and Liu, Y, A theoretical framework for parallel implementation of deep higher order neural networks, Applied Artificial Higher Order Neural Networks for Control and Recognition, Information Science Reference, M Zhang (ed), Hershey PA, USA, pp. 351-361. ISBN 9781522500636 (2016) [Research Book Chapter]

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Copyright 2016 IGI Global

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DOI: doi:10.4018/978-1-5225-0063-6.ch013


This chapter proposes a theoretical framework for parallel implementation of Deep Higher Order Neural Networks (HONNs). First, we develop a new partitioning approach for mapping HONNs to individual computers within a master-slave distributed system (a local area network). This will allow us to use a network of computers (rather than a single computer) to train a HONN to drastically increase its learning speed: all of the computers will be running the HONN simultaneously (parallel implementation). Next, we develop a new learning algorithm so that it can be used for HONN learning in a distributed system environment. Finally, we propose to improve the generalisation ability of the new learning algorithm as used in a distributed system environment. Theoretical analysis of the proposal is thoroughly conducted to verify the soundness of the new approach. Experiments will be performed to test the new algorithm in the future.

Item Details

Item Type:Research Book Chapter
Keywords:artificial neural network, deep neural network, higher order neural network
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Xu, S (Dr Shuxiang Xu)
ID Code:109084
Year Published:2016
Deposited By:Information and Communication Technology
Deposited On:2016-05-19
Last Modified:2019-12-11

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