Masuyama, Naoki and Loo, Chu Kiong and Seera, Manjeevan and Kubota, Naoyuki (2018) Quantum-Inspired Multidirectional Associative Memory With a Self-Convergent Iterative Learning. IEEE Transactions on Neural Networks and Learning Systems, 29 (4). pp. 1058-1068. ISSN 2162-237X, DOI https://doi.org/10.1109/TNNLS.2017.2653114.
Full text not available from this repository.Abstract
Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation. We introduce a quantum-inspired multidirectional associative memory (QMAM) with a one-shot learning model, and QMAM with a self-convergent iterative learning model (IQMAM) based on QHAM in this paper. The self-convergent iterative learning enables the network to progressively develop a resonance state, from inputs to outputs. The simulation experiments demonstrate the advantages of QMAM and IQMAM, especially the stability to recall reliability.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Fuzzy inference; multidirectional associative memory; neural network; quantum-inspired computing (QIC) |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 27 May 2019 05:00 |
| Last Modified: | 27 May 2019 05:00 |
| URI: | http://eprints.um.edu.my/id/eprint/21322 |
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