Dynamical behaviors, circuit design, and synchronization of a novel symmetric chaotic system with coexisting attractors

Hyperchaos of neuron under local active discrete memristor simulating electromagnetic radiation

Memristor enables the coupling of magnetic flux to membrane voltage and is widely used to investigate the response characteristics of neurons to electromagnetic radiation. In this paper, a local active discrete memristor is constructed and used to study the effect of electromagnetic radiation on the dynamics of neurons. The research results demonstrate that increasing electromagnetic radiation intensity could induce hyperchaotic attractors. Furthermore, this neuron model generates hyperchaotic and three points coexistence attractors with the introduction of the memristor. A digital circuit is designed to implement the model and evaluate the randomness of its output sequence. Neuronal models exhibit a rich dynamic behavior with electrical radiation stimulation, which can provide new directions for exploring the production mechanisms of certain neurological diseases.

Chaos and multi-layer attractors in asymmetric neural networks coupled with discrete fractional memristor

This article introduces a novel model of asymmetric neural networks combined with fractional difference memristors, which has both theoretical and practical implications in the rapidly evolving field of computational intelligence. The proposed model includes two types of fractional difference memristor elements: one with hyperbolic tangent memductance and the other with periodic memductance and memristor state described by sine functions. The authenticity of the constructed memristor is confirmed through fingerprint verification. The research extensively investigates the dynamics of a coupled neural network model, analyzing its stability at equilibrium states, studying bifurcation diagrams, and calculating the largest Lyapunov exponents. The results suggest that when incorporating sine memristors, the model demonstrates coexisting state variables depending on the initial conditions, revealing the emergence of multi-layer attractors. The article further demonstrates how the memristor state shifts through numerical simulations with varying memductance values. Notably, the study emphasizes the crucial role of memductance (synaptic weight) in determining the complex dynamical characteristics of neural network systems. To support the analytical results and demonstrate the chaotic response of state variables, the article includes appropriate numerical simulations. These simulations effectively validate the presented findings and provide concrete evidence of the system's chaotic behavior.

Stability analysis of chaotic generalized Lotka-Volterra system via active compound difference anti-synchronization method

This work deals with a systematic approach for the investigation of compound difference anti-synchronization (CDAS) scheme among chaotic generalized Lotka-Volterra biological systems (GLVBSs). First, an active control strategy (ACS) of nonlinear type is described which is specifically based on Lyapunov's stability analysis (LSA) and master-slave framework. In addition, the biological control law having nonlinear expression is constructed for attaining asymptotic stability pattern for the error dynamics of the discussed GLVBSs. Also, simulation results through MATLAB environment are executed for illustrating the efficacy and correctness of considered CDAS approach. Remarkably, our attained analytical outcomes have been in outstanding conformity with the numerical outcomes. The investigated CDAS strategy has numerous significant applications to the fields of encryption and secure communication.