Multilayered Feedforward Neural Network (MLFNN) Architecture as Bidirectional Associative Memory (BAM) for Pattern Storage and Recall
Between two popular ANN architectures – feedforward and feedback, also known as recurrent – feedback architectures have been extensively used for memorization and recall task due to their feedback connections. Due to the inherent simpler dynamics of FNNs, these structures have been explored in the p...
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Published in | Smart and Innovative Trends in Next Generation Computing Technologies pp. 298 - 309 |
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Main Authors | , |
Format | Book Chapter |
Language | English |
Published |
Singapore
Springer Singapore
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Series | Communications in Computer and Information Science |
Subjects | |
Online Access | Get full text |
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Summary: | Between two popular ANN architectures – feedforward and feedback, also known as recurrent – feedback architectures have been extensively used for memorization and recall task due to their feedback connections. Due to the inherent simpler dynamics of FNNs, these structures have been explored in the present work for association task. Variation of standard BP algorithm, two-phase BP algorithm has been proposed for training MLFNNs to behave as associative memory. The results thus collected show that with the proposed algorithm, MLFNN start behaving as associative memory and the recall capability for corrupted versions of the stored patterns is at par with BAM but with lesser time. |
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ISBN: | 9789811086564 9811086567 |
ISSN: | 1865-0929 1865-0937 |
DOI: | 10.1007/978-981-10-8657-1_23 |