An iterative incremental learning algorithm for complex-valued hopfield associative memory
This paper discusses a complex-valued Hopfield associative memory with an iterative incremental learning algorithm. The mathematical proofs derive that the weight matrix is approximated as a weight matrix by the complex-valued pseudo inverse algorithm. Furthermore, the minimum number of iteratio...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/16812/1/99500051.pdf http://eprints.um.edu.my/16812/ |
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| Summary: | This paper discusses a complex-valued Hopfield associative
memory with an iterative incremental learning algorithm. The mathematical
proofs derive that the weight matrix is approximated as a weight
matrix by the complex-valued pseudo inverse algorithm. Furthermore,
the minimum number of iterations for the learning sequence is defined
with maintaining the network stability. From the result of simulation
experiment in terms of memory capacity and noise tolerance, the proposed
model has the superior ability than the model with a complexvalued
pseudo inverse learning algorithm. |
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