Review on memristor application in neural circuit and network
F Yang, J Ma, F Wu - Chaos, Solitons & Fractals, 2024 - Elsevier
Memristor is a nonlinear electronic component with memory properties, it is widely used in a
variety of nonlinear circuits for extensive adaptive control and model description of neural …
variety of nonlinear circuits for extensive adaptive control and model description of neural …
Dynamics analysis and FPGA implementation of discrete memristive cellular neural network with heterogeneous activation functions
The activation function, as an important component of artificial neural networks, endows
neural networks with rich dynamical phenomena by virtue of its nonlinear properties …
neural networks with rich dynamical phenomena by virtue of its nonlinear properties …
Dynamics of heterogeneous Hopfield neural network with adaptive activation function based on memristor
Memristor and activation function are two important nonlinear factors of the memristive
Hopfield neural network. The effects of different memristors on the dynamics of Hopfield …
Hopfield neural network. The effects of different memristors on the dynamics of Hopfield …
Memristive Tabu learning neuron generated multi-wing attractor with FPGA implementation and application in encryption
Memristors, with their unique nonlinear characteristics, are highly suitable for construction
novel neural models with rich dynamic behaviors. In this paper, a memristor with piecewise …
novel neural models with rich dynamic behaviors. In this paper, a memristor with piecewise …
Dynamic analysis and hardware implementation of multi-scroll Hopfield neural networks with three different memristor synapses
F Yu, C Wu, Y Lin, S He, W Yao, S Cai, J Jin - Nonlinear Dynamics, 2024 - Springer
Neurons play an important role in forming behaviors and cognition through synaptic
interactions. When organized into neural networks, these neurons can exhibit complex …
interactions. When organized into neural networks, these neurons can exhibit complex …
Analysis of the dynamical behavior of discrete memristor-coupled scale-free neural networks
W Deng, M Ma - Chinese Journal of Physics, 2024 - Elsevier
The synchronization of neural networks is crucial for neural information processing and
represents a key feature of various functional brain diseases. Memristors are ideal electronic …
represents a key feature of various functional brain diseases. Memristors are ideal electronic …
Novel hyperchaotic image encryption method using machine learning-RBF
S Zhou, H Zhang, Y Zhang, H Zhang - Nonlinear Dynamics, 2024 - Springer
In this paper, we put forward a novel hyperchaotic image encryption method using machine
learning-RBF. First, a new 4D continuous hyperchaotic system is designed to address the …
learning-RBF. First, a new 4D continuous hyperchaotic system is designed to address the …
[HTML][HTML] Design and Analysis of a Novel Fractional-Order System with Hidden Dynamics, Hyperchaotic Behavior and Multi-Scroll Attractors
F Yu, S Xu, Y Lin, T He, C Wu, H Lin - Mathematics, 2024 - mdpi.com
The design of chaotic systems with complex dynamic behaviors has always been a key
aspect of chaos theory in engineering applications. This study introduces a novel fractional …
aspect of chaos theory in engineering applications. This study introduces a novel fractional …
Single direction, grid and spatial multi-scroll attractors in Hopfield neural network with the variable number memristive self-connected synapses
Q Wan, Q Yang, T Liu, C Chen, K Shen - Chaos, Solitons & Fractals, 2024 - Elsevier
Due to the synapse-like nonlinearity and memory characteristics, the memristor is often used
to simulate the biological neural synapse. In this paper, a family of three-neuron Hopfield …
to simulate the biological neural synapse. In this paper, a family of three-neuron Hopfield …
Nonlinear chaotic Lorenz-Lü-Chen fractional order dynamics: A novel machine learning expedition with deep autoregressive exogenous neural networks
This exhaustive study entails fractional processing of the unified chaotic Lorenz-Lü-Chen
attractors using machine learning expedition with Levenberg-Marquardt optimized deep …
attractors using machine learning expedition with Levenberg-Marquardt optimized deep …