基于混沌激光产生1 Gbit/s的随机数

利用光反馈半导体激光器产生的混沌激光作为随机数发生器的物理熵源,通过8位 ADC将熵源信息转化为二进制码,并经后续差分运算处理改善其随机性,最终获得了1 Gbit/s的随机数.所产生的随机数通过了NIST Special Publication 800-22的全部测试项. 关键词： 混沌激光 随机数发生器 半导体激光器 模数转换     

受扰统一混沌系统基于RBF网络的主动滑模控制

针对受外扰影响的统一混沌系统,提出一种基于径向基函数(RBF)神经网络的主动滑模自适应控制方法.将被控系统分解为受控子系统和自由子系统,利用主动控制思想,建立受控子系统在目标点处的状态误差的可控标准型,设计出一个结构简单的基于滑模趋近率在线参数整定的RBF函数神经网络控制器,并且基于Lyapunov稳定性理论分析了系统的稳定性.仿真结果表明该控制器对系统参数突变和外部干扰具有鲁棒性,同时抑制了抖振. 关键词： 统一混沌系统 主动控制 滑模控制 RBF网络     

Duffing单边碰撞系统的混沌鞍合并激变

以典型的Duffing单边碰撞系统为研究对象,对系统中的混沌鞍进行了细致的分析.研究表明,系统的混沌鞍同样存在合并激变,合并激变是由连接两个混沌鞍的周期鞍的稳定流形与不稳定流形相切所诱发,相切使得边界上的混沌鞍与内部的混沌鞍发生碰撞而突然合并为一个较大的边界混沌鞍.混沌鞍的合并激变行为最终会诱导混沌吸引子的合并激变发生. 关键词： Duffing碰撞系统 混沌鞍 周期鞍 稳定与不稳定流形     

基于互注入半导体激光器的混沌输出产生17.5 Gbit/s随机码

采用两个借助光纤连接的相互注入半导体激光器,实验获取了10 GHz超宽带混沌种子信号.通过8-bit模数转换器将混沌信号转换为二进制数据流,并进行进一步的逻辑异或处理和舍弃最高有效位操作,最终获得了能顺利通过美国国家标准与技术研究院(National Institute of Standard and Technology,简记为NIST)800-22标准测试以及Diehard测试,速率达17.5 Gbit/s的高速随机码. 关键词： 随机码 混沌激光 互注入半导体激光器     

基于区间系统理论的分数阶混沌系统同步

通过设计一个非线性反馈控制器,实现了分数阶混沌系统的同步.与其他的分数阶混沌系统同步方法相比,提出的控制器设计方法保留了部分误差系统中的非线性项,而没有完全抵消同步误差系统的非线性项,有效改善了误差系统的控制性能.同时,应用区间分数阶线性时不变系统稳定性原理和线性矩阵不等式技术,得到了一个新的分数阶混沌系统同步的充分条件,进而获得的控制器保证了混沌系统同步.仿真结果验证了提出方法的有效性. 关键词： 区间分数阶时不变系统 分数阶混沌系统 混沌同步     

Simulation of ion beam extraction and focusing system

The characteristics of ion beam extraction and focused to a volume as small as possible were investigated with the aid of computer code SIMION 3D version 7. This has been used to evaluate the extraction characteristics (accel-decel system) to generate an ion beam with low beam emittance and high brightness. The simulation process can provide a good study for optimizing the extraction and focusing system of the ion beam without any losses and transported to the required target. Also, a study of a simulation model for the extraction system of the ion source was used to describe the possible plasma boundary curvatures during the ion extraction that may be affected by the change in an extraction potential with a constant plasma density meniscus.     

Comment on “A constructive proof on the existence of globally exponentially attractive set and positive invariant set of general Lorenz family”, P. Yu,X.X. Liao,S.L. Xie,Y.L. Fu [Commun Nonlinear Sci Numer Simulat 14 (2009) 2886–2896]

In the commented paper the authors study some aspects of boundedness in the general Lorenz family, $\dot{x}=\sigma (y-x)\text{,}\dot{y}=\rho x-\gamma y-\mathit{xz}\text{,}\dot{z}=-\beta z+\mathit{xy}$, considering that it contains four independent parameters. However, as we show here by means of a linear scaling in time and coordinates, they are dealing with a system homothetically equivalent to the Lorenz system. Consequently, the novel and interesting results they provide for the general Lorenz family can be obtained working directly with the Lorenz equations, that is, dealing only with three independent parameters.     

Parameter space of the Rulkov chaotic neuron model

The parameter space of the two dimensional Rulkov chaotic neuron model is taken into account by using the qualitative analysis, the co-dimension 2 bifurcation, the center manifold theorem, and the normal form. The goal is intended to clarify analytically different dynamics and firing regimes of a single neuron in a two dimensional parameter space. Our research demonstrates the origin that there exist very rich nonlinear dynamics and complex biological firing regimes lies in different domains and their boundary curves in the two dimensional parameter plane. We present the parameter domains of fixed points, the saddle-node bifurcation, the supercritical/subcritical Neimark–Sacker bifurcation, stability conditions of non hyperbolic fixed points and quasiperiodic solutions. Based on these parameter domains, it is easy to know that the Rulkov chaotic neuron model can produce what kinds of firing regimes as well as their transition mechanisms. These results are very useful for building-up a large-scale neuron network with different biological functional roles and cognitive activities, especially in establishing some specific neuron network models of neurological diseases.