The characteristics of nonlinear chaotic dynamics in quantum cellular neural networks

With the polarization of quantum-dot cell and quantum phase serving as state variables, this paper does both theoretical analysis and simulation for the complex nonlinear dynamical behaviour of a three-cell-coupled Quantum Cellular Neural Network （QCNN）, including equilibrium points, bifurcation and chaotic behaviour. Different phenomena, such as quasi-periodic, chaotic and hyper-chaotic states as well as bifurcations are revealed. The system＇s bifurcation and chaotic behaviour under the influence of the different coupling parameters are analysed. And it finds that the unbalanced cells coupled QCNN is easy to cause chaotic oscillation and the system response enters into chaotic state from quasi-periodic state by quasi-period bifurcation; however, the balanced cells coupled QCNN also can be chaotic when coupling parameters is in some region. Additionally, both the unbalanced and balanced cells coupled QCNNs can possess hyper-chaotic behaviour. It provides valuable information about QCNNs for future application in high-parallel signal processing and novel ultra-small chaotic generators.     

Synchronization and coherence resonance in chaotic neural networks

Synchronization and coherence of chaotic Morris--Lecar (ML) neural networks have been investigated by numerical methods. The synchronization of the neurons can be enhanced by increasing the number of the shortcuts, even though all neurons are chaotic when uncoupled. Moreover, the coherence of the neurons exhibits a non-monotonic dependence on the density of shortcuts. There is an optimal number of shortcuts at which the neurons' motion is most ordered, i.e. the order parameter (the characteristic correlation time) that is introduced to measure the coherence of the neurons has a maximum. These phenomena imply that stochastic shortcuts can tame spatiotemporal chaos. The effects of the coupling strength have also been studied. The value of the optimal number of shortcuts goes down as the coupling strength increases.     

混沌时滞神经网络系统的反同步

利用状态观测器方法研究了一类带时滞的混沌神经网络系统的反同步问题.与应用于其他混沌系统的反同步方法相比,提出的方法更为简便,并且利用极点配置技术,只要通过调整特征值来实现反同步速率的快慢.最后,给出了数值例子和计算机仿真结果来验证该方案的有效性. 关键词： 混沌神经网络 状态观测器 极点配置技术 反同步     

Short-term memory in orthogonal neural networks

We study the ability of linear recurrent networks obeying discrete time dynamics to store long temporal sequences that are retrievable from the instantaneous state of the network. We calculate this temporal memory capacity for both distributed shift register and random orthogonal connectivity matrices. We show that the memory capacity of these networks scales with system size.     

Synchronization of coupled chaotic dynamics on networks

We review some recent work on the synchronization of coupled dynamical systems on a variety of networks. When nodes show synchronized behaviour, two interesting phenomena can be observed. First, there are some nodes of the floating type that show intermittent behaviour between getting attached to some clusters and evolving independently. Secondly, two different ways of cluster formation can be identified, namely self-organized clusters which have mostly intra-cluster couplings and driven clusters which have mostly inter-cluster couplings.     

Subharmonic bifurcations and chaotic dynamics of an air damping completely inelastic bouncing ball

We investigate the dynamics of a plastic ball on a vibrated platform in air by introducing air damping effect into the completely inelastic bouncing ball model. The air damping gives rise to larger saddle-node bifurcation points and a chaos confirmed by the largest Lyapunov exponent of a one-dimensional discrete mapping. The calculated bifurcation point distribution shows that the periodic motion of the ball is suppressed and a chaos emerges earlier for an increasing air damping. When the reset mechanism and the linear stability which cause periodic motion of the ball both collapse, the investigated system is fully chaotic.     

Random recurrent neural networks dynamics

This paper is a review dealing with the study of large size random recurrent neural networks. The connection weights are varying according to a probability law and it is possible to predict the network dynamics at a macroscopic scale using an averaging principle. After a first introductory section, the section 2 reviews the various models from the points of view of the single neuron dynamics and of the global network dynamics. A summary of notations is presented, which is quite helpful for the sequel. In section 3, mean-field dynamics is developed. The probability distribution characterizing global dynamics is computed. In section 4, some applications of mean-field theory to the prediction of chaotic regime for Analog Formal Random Recurrent Neural Networks (AFRRNN) are displayed. The case of AFRRNN with an homogeneous population of neurons is studied in section 4.1. Then, a two-population model is studied in section 4.2. The occurrence of a cyclo-stationary chaos is displayed using the results of [16]. In section 5, an insight of the application of mean-field theory to IF networks is given using the results of [9].     

Multistability in bidirectional associative memory neural networks

In this Letter, the multistability issue is studied for Bidirectional Associative Memory (BAM) neural networks. Based on the existence and stability analysis of the neural networks with or without delay, it is found that the 2n-dimensional networks can have n³ equilibria and n² equilibria of them are locally exponentially stable, where each layer of the BAM network has n neurons. Furthermore, the results has been extended to (n+m)-dimensional BAM neural networks, where there are n and m neurons on the two layers respectively. Finally, two numerical examples are presented to illustrate the validity of our results.     

Synchronization-based approach for parameters identification in delayed chaotic neural networks

In this paper, an adaptive procedure to the problem of synchronization and parameters identification for chaotic neural networks with time-varying delay is introduced by combining the adaptive control and linear feedback with appropriate update law. Based on the invariance principle of functional differential equations, all the connection weight matrices can be efficiently estimated according to a simple, rigorous, and systematic technique. This approach is also able to track the changes in the operating parameters of the experimental neural networks rapidly. The speed of synchronization and parameters estimation can be adjusted under the adaptive gain properly chosen. In addition, the method is simple to implement in practice, and it is quite robust against the effect of slight noise in the given time series and the estimated value of a parameter fluctuates around the correct value.     

Stable irregular dynamics in complex neural networks

Irregular dynamics in multidimensional systems is commonly associated with chaos. For infinitely large sparse networks of spiking neurons, mean field theory shows that a balanced state of highly irregular activity arises under various conditions. Here we analytically investigate the microscopic irregular dynamics in finite networks of arbitrary connectivity, keeping track of all individual spike times. For delayed, purely inhibitory interactions we demonstrate that any irregular dynamics that characterizes the balanced state is not chaotic but rather stable and convergent towards periodic orbits. These results highlight that chaotic and stable dynamics may be equally irregular.     

A global qualitative view of bifurcations and dynamics in the Rössler system

The aim of the Letter is a global study of the well-known Rössler system to point out the main complex dynamics that it can exhibit. The structural analysis is based on the periodic solutions of the system investigated by a harmonic balance technique. Simplified expressions of such limit cycles are first derived and characterized, then their local bifurcations are denoted, also giving indications to predict possible homoclinic orbits with the same unifying approach. These analytical results give a general picture of the system behaviours in the parameter space and numerical analysis and simulations confirm the qualitative accuracy of the whole. Such predictions have also an important role in applying efficiently the above numerical procedures.     

一类混沌神经网络的全局同步

研究了一类时滞混沌神经网络的全局同步问题.应用驱动-响应同步方法和线性矩阵不等式技术，给出了时滞混沌神经网络全局同步的充分条件和同步控制器设计方法，而且所得到的控制器易于实现.仿真示例验证了本文方法的有效性. 关键词： 混沌神经网络 同步 驱动-响应法 线性矩阵不等式     

Chaotic dynamics of high-order neural networks

The dynamics of an extremely diluted neural network with high-order synapses acting as corrections to the Hopfield model is investigated. The learning rules for the high-order connections contain mixing of memories, different from all the previous generalizations of the Hopfield model. The dynamics may display fixed points or periodic and chaotic orbits, depending on the weight of the high-order connections , the noise levelT, and the network load, defined as the ratio between the number of stored patterns and the mean connectivity per neuron, =P/C. As in the related fully connected case, there is an optimal value of the weight that improves the storage capacity of the system (the capacity diverges).     

Equilibrium bifurcations and chaotic transitions in coupled microlaser lattices

Analytic and simulation studies for the steady-state equilibria and bifurcations of coupled microlaser arrays are described. Lateral cavity interactions affect the gain in each cavity, leading to active photonic lattice behavior, equivalent to a nonlinear coupled oscillator lattice. The coupled-cavity rate equations are employed to follow the coherent photon and carrier population in each lattice site. Fixed-point-type steady states, of constant lattice phase shift, result for low coupling strengths; the radiation envelope for these states conforms with a periodic Bloch state over the array. Bifurcations to limit cycles of increasing complexity occur at higher coupling via period doubling sequences. The associated spatial patterns of photon and carrier lattice distribution resemble photonic convection cells. Limit cycles of different periods, emanating mathematically from different original fixed points, coexist at high strengths, each one accessible from different initial conditions. The multiplicity of possible limit cycles in systems with many degrees of freedom (number of lattice sites) combined with changes in their accessibility from initial conditions offers new insights to chaotic transitions, compared to low dimensionality paradigms.     

Public channel cryptography by synchronization of neural networks and chaotic maps

Two different kinds of synchronization have been applied to cryptography: synchronization of chaotic maps by one common external signal and synchronization of neural networks by mutual learning. By combining these two mechanisms, where the external signal to the chaotic maps is synchronized by the nets, we construct a hybrid network which allows a secure generation of secret encryption keys over a public channel. The security with respect to attacks, recently proposed by Shamir et al., is increased by chaotic synchronization.     

基于混沌神经网络的盲检测改进新算法

针对Hopfield神经网络的多起点问题,提出了一种新的基于混沌神经网络的盲信号检测算法,实现了二进制移相键控信号盲检测.据此进一步提出双sigmoid混沌神经网络模型,构造了新的能量函数,且证明了该模型的稳定性,并对网络参数进行配置.仿真实验表明:混沌神经网络能够避免局部极小点且具备较强的抗噪性能,双sigmoid混沌神经网络则继承了其所有的优点,且其收敛速度更快,仅需更短的接收数据即可到达全局真实平衡点,从而降低了算法的计算复杂度,减少了运行时间.     

Robust anti-synchronization of a class of delayed chaotic neural networks

This paper deals with the anti-synchronization problem of a class of delayed neural networks. Based on the Lyapunov stability theory and the Halanay inequality lemma, a kind of controller is designed. It is proved that this kind of controller can achieve anti-synchronization of neural networks with delays. Numerical simulations demonstrate the effectiveness and robustness of the proposed anti-synchronization scheme.     

Robust adaptive synchronization of chaotic neural networks by slide technique

In this paper, we focus on the robust adaptive synchronization between two coupled chaotic neural networks with all the parameters unknown and time-varying delay. In order to increase the robustness of the two coupled neural networks, the key idea is that a sliding-mode-type controller is employed. Moreover, without the estimate values of the network unknown parameters taken as an updating object, a new updating object is introduced in the constructing of controller. Using the proposed controller, without any requirements for the boundedness, monotonicity and differentiability of activation functions, and symmetry of connections, the two coupled chaotic neural networks can achieve global robust synchronization no matter what their initial states are. Finally, the numerical simulation validates the effectiveness and feasibility of the proposed technique.