Title: Optimisation of Hopfield networks for storage and recall: a decade review

Authors: Jay Kant Pratap Singh Yadav; Arun Kumar Yadav; Divakar Yadav; Vikash Yadav

Addresses: Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, Uttar Pradesh, India ' Department of Computer Science and Engineering, National Institute of Technology, Hamirpur, India ' Department of Computer Science and Engineering, National Institute of Technology, Hamirpur, India ' Department of Technical Education, Uttar Pradesh, India

Abstract: Pattern storage and recall in an efficient and effective manner is a prominent task in the pattern recognition field. Recurrent (also called feedback) networks are most frequently used network that can store and recall the patterns. Recurrent networks have a capability of recalling noisy or partial patterns like brain. A detailed study of different neural network, it is found that Hopfield neural network outperforms as compared to others. In this paper, we illustrate the review of a decade on optimisation of Hopfield neural network to improve storage capacity and recalling of patterns.

Keywords: Hopfield neural network; genetic algorithm; cross-association; quadbit.

DOI: 10.1504/IJAIP.2022.122197

International Journal of Advanced Intelligence Paradigms, 2022 Vol.21 No.3/4, pp.321 - 329

Received: 11 May 2019
Accepted: 05 Jun 2019
Published online: 12 Apr 2022 *

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