Anti-synchronization of a M-Hopfield neural network with generalized hyperbolic tangent activation function - Viera-Martin, E.; Gómez-Aguilar, J. F.; Solís-Pérez, J. E.; Hernández-Pérez, J. A.; Olivares-Peregrino, V. H. (The European physical journal. ST, Special topics, 2022)
This paper analyzes non-integer Hopfield neural network dynamics introducing the hyperbolic tangent transfer function generalized by the Mittag-Leffler function and the M-truncated derivative with constant and variable order. The novel neural network’s (ANN) behaviors are studied through their dynamics depicted in phase portraits and the 0-1 test to determine where the ANN displays strong chaotic behaviors. According to the numerical results, the generalized Hopfield (M-HNTF) reveals weak chaotic dynamics with constant values under 0.99 and regular behaviors lower than 0.8. Considering the variable order, the chaotic behaviors depend on the decay rate of the time-varying function. Due to this, we got systems with weak chaotic dynamics until strong chaotic dynamics. Next, we used two scenarios to anti-synchronize a system master and a slave system. The first considering a dynamic, chaotic system and a regular system, the second: two M-HNTF with variable order. Numerical results illustrate those mentioned above, showing the control aim. Getting new chaotic dynamics from non-integer systems with variable order is essential to develop protocols to offer secure communications, new random number generators, image encrypts schemes, to name a few.