Test Bed for Multilayered Feed forward Neural Network Architectures as Bidirectional Associative Memory
Manisha Singh. Article: Test Bed for Multilayered Feed forward Neural Network Architectures as Bidirectional Associative Memory. IJCA Proceedings on National Conference on Next generation Computing Technologies and Applications NGCTA:21-24, November 2013. Full text available. BibTeX
@article{key:article,
author = {Manisha Singh},
title = {Article: Test Bed for Multilayered Feed forward Neural Network Architectures as Bidirectional Associative Memory},
journal = {IJCA Proceedings on National Conference on Next generation Computing Technologies and Applications},
year = {2013},
volume = {NGCTA},
pages = {21-24},
month = {November},
note = {Full text available}
}
Abstract
Multilayered feed-forward neural networks are considered universal approximators and hence extensively been used for function approximation. Function approximation is an instance of supervised learning which is one of the most studied topics in machine learning, artificial neural networks, pattern recognition, and statistical curve fitting. Bidirectional associative memory is another class of networks which has been used for approximating various functions. In the present study, an approach for using MLFNN architectures as BAM with BP learning has been proposed and initially been tested on certain functions. The results obtained are analyzed and presented.
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