An iterative incremental learning algorithm for complex-valued hopfield associative memory

Masuyama, N. and Chu, K.L. (2016) An iterative incremental learning algorithm for complex-valued hopfield associative memory. In: The 23rd International Conference on Neural Information Processing (ICONIP 2016), 17 - 21 October 2016, Kyoto, Japan.

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Associative memory; Complex-valued model; Incremental learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Mr. Mohd Safri
Date Deposited: 17 Jan 2017 07:01
Last Modified: 17 Jan 2017 07:01
URI: http://eprints.um.edu.my/id/eprint/16812

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