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:
| 主要作者: | |
|---|---|
| 其他作者: | |
| 格式: | Theses and Dissertations |
| 語言: | English |
| 出版: |
2009
|
| 主題: | |
| 在線閱讀: | http://hdl.handle.net/10356/19826 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | 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. |
|---|