Suitable adjustment methods are crucial for enhancing the network's dynamics, while inappropriate applications can lead to the loss of its chaotic characteristics. The study explores the dynamic modulation of HNN via time-variant stimuli.
Adjusting dynamics of the Hopfield Neural Network through time-variant stimuli is crucial for enhancing network behavior and security applications.
Hopfield Neural Network dynamics can be effectively modulated using time-variant stimuli, enhancing network behavior and enabling secure communication technologies.