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A theoretical framework for parallel implementation of deep higher order neural networks
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.
History
Publication title
Applied Artificial Higher Order Neural Networks for Control and RecognitionEditors
M ZhangPagination
351-361ISBN
9781522500636Department/School
School of Information and Communication TechnologyPublisher
Information Science ReferencePlace of publication
Hershey PA, USAExtent
18Rights statement
Copyright 2016 IGI GlobalRepository Status
- Restricted