Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/19826
Title: Parallel implementation of backpropagation neural networks : a study of network-based parallelism
Authors: Arularasan Ramasamy.
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 1997
Abstract: Artificial neural networks have applications in many fields ranging from medicine to image processing. One of the most popular neural network architecture and learning algorithm is the multi-layer feedforward architecture where the Backpropagation (BP) learning scheme is used. Although the BP algorithm is popular, training takes a very long time for large neural networks with a large training set. Training can be sped by parallelising the BP algorithm on a parallel machine. This thesis presents a detailed study of network-based parallelisation of the BP algorithm on message passing multi-computers. In this scheme, the neural network is vertically sliced and distributed among the processing elements connected in a ring topology.
URI: http://hdl.handle.net/10356/19826
Rights: NANYANG TECHNOLOGICAL UNIVERSITY
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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