Synchronization of different chaotic systems via active radial basis functions sliding mode controller

This paper presents a new method to synchronize different chaotic systems with disturbances via an active radial basis function （RBF） sliding controller. This method incorporates the advantages of active control, neural network and sliding mode control. The main part of the controller is given based on the output of the RBF neural networks and the weights of these single layer networks are tuned on-line based on the sliding mode reaching law. Only several radial basis functions are required for this controller which takes the sliding mode variable as the only input. The proposed controller can make the synchronization error converge to zero quickly and can overcome external disturbances. Analysis of the stability for the controller is carried out based on the Lyapunov stability theorem. Finally, five examples are given to illustrate the robustness and effectiveness of the proposed synchronization control strategy.     

Cascades with coupled map lattices in preferential attachment community networks

In this paper, cascading failure is studied by coupled map lattice （CML） methods in preferential attachment community networks. It is found that external perturbation R is increasing with modularity Q growing by simulation. In particular, the large modularity Q can hold off the cascading failure dynamic process in community networks. Furthermore, different attack strategies also greatly affect the cascading failure dynamic process. It is particularly significant to control cascading failure process in real community networks.     

Controlling bifurcations and chaos in discrete small-world networks

We propose an impulsive hybrid control method to control the period-doubling bifurcations and stabilize unstable periodic orbits embedded in a chaotic attractor of a small-world network. Simulation results show that the bifurcations can be delayed or completely eliminated. A periodic orbit of the system can be controlled to any desired periodic orbit by using this method.     

非线性函数耦合的Chen吸引子网络的混沌同步

利用非对称非线性函数耦合混沌同步方法,讨论了Chen吸引子的混沌同步问题,数值模拟分析初始值和耦合强度因子的选择对于实现混沌同步的影响. 将非对称非线性函数耦合同步方法进一步推广发展到完全连接网络和由星形子网络构成的复杂大网络混沌同步的研究中. 提供了确定网络中神经元之间混沌同步状态稳定性的误差发展方程,并讨论各个耦合强度因子对网络同步稳定性过程的影响,给出了相应的稳定性范围. 通过数值模拟证明利用非线性函数作为耦合函数,实现完全连接网络、星形子网络构成大网络的混沌同步是有效的. 可以预测在网络的混沌同步 关键词： 非线性耦合函数 Chen吸引子 混沌同步 网络     

推广的失活网络动力学同步优化

在不改变网络度分布的条件下,研究了推广的失活网络的同步行为. 应用特征值比R来衡量网络的同步能力,发现同步能力可以通过改变结构参数——激活节点数M来进行优化.特征值比R随M的变化非常敏感,激活节点数M越大,特征值比R越小,同步能力就越强,且在一定范围内遵循R～M^-2.0的幂律关系.通过引入结构微扰,该网络的同步能力也可以得到有效优化. 关键词： 推广的失活网络 同步 特征值比 优化     

双面声场反向神经网络声压外推分离

针对有限测量孔径造成的窗效应和离散传声器阵列带来的卷绕误差对双面声场分离技术的影响,提出了一种利用反向传播神经网络的全息面声压外推方法。该方法首先利用孔径内部测点平面坐标和相应声压值组成的学习样本训练神经网络,拟合出两者的函数关系。接着代入孔径外部测点坐标得到对应声压值,实现有限孔径外推。最后将已有测量值和外推测量值组成的虚拟大孔径导入双面声场分离处理。与传统外推方法相比,该方法不需要先验知识,操作简单,计算抗干扰能力强,结果准确性高。数值仿真和实验进一步验证了该方法在改善声场分离结果方面的可行性和有效性。     

神经网络在棱镜摄谱仪定量分析光谱中的应用

给出了采用BP神经网络拟合标准光源在棱镜摄谱仪的谱平面位置和相应波长的非线性函数,省去了和标准铁谱比对测量未知谱线波长,简化了光谱的定量分析,提高了仪器的自动化和智能化水平.     

北京谱仪III上利用神经网络方法进行粒子鉴别的研究

本文描述了一种多层感知器的神经网络系统在BESIII粒子鉴别技术中的应用。网络按照子探测器分别进行训练, 输出结果可以作为后续网络的输入或者可以为似然函数方法构建概率密度函数。蒙特卡罗模拟样本的检验结果表明, 利用神经网络方法可以在BESIII上获得较好的粒子鉴别效果。     

采用中子衍射实验解析PbS的原子间力常数

使用日本原子能研究开发机构里设置的高分解能粉末中子衍射装置, 在 15 K 和 294 K 温度下对 PbS 进行了中子衍射实验。在 15 K 和 294 K 温度里观察到了明显的振动形状的热漫散射。 所观察到的漫散射强度可由原子间热振动相关效应的影响来解释。在 294 K 下从原子间相关效应和德拜—劳厄因子的温度参数中获得了 PbS 相邻原子间的力常数。     

Information filtering in evolving online networks

Recommender systems use the records of users' activities and profiles of both users and products to predict users' preferences in the future. Considerable works towards recommendation algorithms have been published to solve the problems such as accuracy, diversity, congestion, cold-start, novelty, coverage and so on. However, most of these research did not consider the temporal effects of the information included in the users' historical data. For example, the segmentation of the training set and test set was completely random, which was entirely different from the real scenario in recommender systems. More seriously, all the objects are treated as the same, regardless of the new, the popular or obsoleted products, so do the users. These data processing methods always lose useful information and mislead the understanding of the system's state. In this paper, we detailed analyzed the difference of the network structure between the traditional random division method and the temporal division method on two benchmark data sets, Netflix and MovieLens. Then three classical recommendation algorithms, Global Ranking method, Collaborative Filtering and Mass Diffusion method, were employed. The results show that all these algorithms became worse in all four key indicators, ranking score, precision, popularity and diversity, in the temporal scenario. Finally, we design a new recommendation algorithm based on both users' and objects' first appearance time in the system. Experimental results showed that the new algorithm can greatly improve the accuracy and other metrics.