Parallel implementation of backpropagation neural networks : a study of network-based parallelism
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 algori...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2009
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/19826 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
|---|