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 complex-valued pseudo inverse learning algorithm.
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Acknowledgments
The authors would like to acknowledge a scholarship provided by the University of Malaya (Fellowship Scheme). This research is supported by High Impact Research UM.C/625/1/HIR/MOHE/FCSIT/10 from University of Malaya.
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Masuyama, N., Loo, C.K. (2016). An Iterative Incremental Learning Algorithm for Complex-Valued Hopfield Associative Memory. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_51
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DOI: https://doi.org/10.1007/978-3-319-46681-1_51
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