Eur. Phys. J. Special Topics 227, 811–820 (2018)
https://doi.org/10.1140/epjst/e2018-800018-7

Regular Article

A Hopfield neural network with multiple attractors and its FPGA design

¹ Center for Nonlinear Dynamics, College of Engineering, Defence University, Bishoftu, Ethiopia
² Faculty of Electronics Sciences, Autonomous University of Puebla, 72570 Puebla, Mexico
³ Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
⁴ Department of Information Technology, Faculty of Computing and IT, King Abdulaziz University, Jeddah, Saudi Arabia
⁵ Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia

^a e-mail: phamvietthanh@tdt.edu.vn

Received: 11 February 2018
Received in final form: 31 March 2018
Published online: 19 October 2018

Abstract

Neural network is important for a wide range of applications. Especially, a small neural network can display various complex behaviors. In this work, the investigations of a Hopfield neural network and its field programmable gate array (FPGA) implementation have been reported. The considered Hopfield neural network is simple because it includes only three neurons. It is interesting that we observed chaos and numerous coexisting attractors in such a network. In addition, the network has been implemented via an FPGA platform to verify its feasibility.