DocumentCode
3312653
Title
Matrix realisations of multilayer perceptron ANN
Author
Chidzonga, It F.
Author_Institution
Dept. of Electr. Eng., Zimbabwe Univ., Harare, Zimbabwe
fYear
1997
fDate
9-10 Sep 1997
Firstpage
181
Lastpage
186
Abstract
The backpropagation neural network training algorithm is formulated via matrix transformations as opposed to the usual indexed algebraic scalar approach. This formulation allows for easy visualisation and understanding of error propagation and the consequent weight adjustment. The so configured network can be readily unravelled for further study if required. Illustrative results on simple and concise Matlab simulation are presented
Keywords
backpropagation; matrix algebra; multilayer perceptrons; neural net architecture; Matlab simulation; backpropagation neural network training algorithm; error propagation; matrix realisations; matrix transformations; multilayer perceptron ANN; multilayer perceptron architecture; visualisation; weight adjustment; Artificial neural networks; Error correction; Filtering; Neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Communications and Signal Processing, 1997. COMSIG '97., Proceedings of the 1997 South African Symposium on
Conference_Location
Grahamstown
Print_ISBN
0-7803-4173-2
Type
conf
DOI
10.1109/COMSIG.1997.630006
Filename
630006
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