A new class of Hopfield neural network with double memristive synapses and its DSP implementation
School of Information Science and Engineering, Dalian polytechnic University, 116034, Dalian, China
Accepted: 1 October 2022
Published online: 14 October 2022
The nonlinear characteristics are studied in a new 4D Hopfield neural network model with two nonlinear synaptic weights in this paper. The synaptic function is modeled by introducing memristors in the Hopfield neural network system (HNNs) model. And on the second neuron, the addition of external stimulus currents was considered. The dissipation of this neural network model was studied and Lyapunov functions were introduced to prove the boundedness of the system. Rich and complex dynamical behaviors of the HNNs were analyzed through phase diagrams, bifurcation diagrams, and Lyapunov exponential spectrum. The studied nonlinear dynamical behavior included periodic windows, chaos, and in particular coexistence of asymmetric attractors. In addition, an implementation of the hardware circuit for these HNNs was performed on a DSP platform, and it was shown to be in good consistent performance with the Matlab simulations. This work has provided a theoretical basis for the application of HNNs to human brain dynamics.
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