Design and implementation of a new fractional-order Hopfield neural network system

Abstract

In this work, a novel chaotic system of fractional-order based on the model of Hop-field Neural Network (HNN) is proposed. The numerical solutions of the 4-neurons-based HNN fractional-order chaotic system are obtained by using the Adomain decomposition method. The dynamical performances of the the 4-neurons-based HNN fractional-order chaotic system are explored through attractor trajectories, bifurcation diagrams, Lyapunov exponents, SE complexity and chaotic diagram based on SE complexity. In addition, the 4-neurons-based HNN fractionalorder chaotic system is circuit implemented based on the Multisim platform. The experimental results indicate that the 4-neurons-based HNN fractional-order chaotic system has rich dynamic behavior, and the influence of different order on the dynamical properties of the system are particularly great. This research will provide theoretical foundation and experimental basis for the hardware implementation and application of the 4-neurons-based HNN fractional-order chaotic system.

A new 3D fractional-order chaotic system with complex dynamics

“…In addition, it can provide more information capacity and transmission capability, which is of great significance in information processing and communication. Fractional-order chaotic systems have been applied in many scientific fields in recent years, including neural networks [20][21][22][23], economics [24], mechanics [25], and chaotic systems [26][27][28]. The chaotic system has flexibility [29].…”

Analysis of neural network connections based on memristors and their multiple offset phenomena

“…While either a conservative or a dissipative system's trajectory seems to be random movement, both exhibit rich dynamical behavior and are common in the physical world. In order to learn more about the characteristics of chaotic systems, academics have recently done significant and in-depth research on them [6][7][8].…”