应用荧光光谱和径向基函数神经网络定量检测三聚氰胺

实验发现三聚氰胺溶液在紫外光激发下产生较强荧光,测得其荧光峰在310～600 nm之间,荧光峰值波长为420 nm左右,荧光相对强度与三聚氰胺溶液浓度呈现复杂的非线性关系。提出了采用径向基函数神经网络结合荧光光谱对三聚氰胺溶液浓度进行测定的方法。对每个样本选取30个发射波长值所对应的荧光强度作为网络数据,训练、建立了径向基函数神经网络。应用训练好的径向基函数神经网络,对5种三聚氰胺溶液的浓度进行预测,结果相对误差分别为0.93%,0.09%,0.31%,1.55%,4.61%。该方法能快捷、准确地测定三聚氰胺在溶液中的含量,为三聚氰胺检测及食品安全监管提供了一种新方法。     

移动Ad Hoc网络动力学同步能力的研究

研究了移动Ad Hoc网络的动力学同步行为.用特征值比R来衡量网络的同步能力,特征值比R越小,同步能力越强.结果表明最初该网络的特征值比R随聚类系数C的增加而增加,继续增大聚类系数,特征值比反而减小.特征值比随聚类系数并不是普遍的正比关系也不是反比关系,而是先增大后减小,明显存在一个极值.     

Optical Communications Research Activities at COM RD1 Siemens S.A.

Abstract

This article summarizes the research activities carried out by the R&D Optical Networks Communications group (COM RD1), at Siemens S.A., in collaboration with both universities and other research institutes. These activities cover various aspects of advanced modulation formats, all optical wavelength conversion, optical quality of signal monitoring in transparent WDM networks, routing, and wavelength assignment in optical networks.     

Free-matrix-based time-dependent discontinuous Lyapunov functional for synchronization of delayed neural networks with sampled-data control

This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopted in constructing the Lyapunov functional, which takes advantage of the sampling characteristic of sawtooth input delay. Based on this discontinuous Lyapunov functional, some less conservative synchronization criteria are established to ensure that the slave system is synchronous with the master system. The desired sampled-data controller can be obtained through the use of the linear matrix inequality(LMI) technique. Finally, two numerical examples are provided to demonstrate the effectiveness and the improvements of the proposed methods.     

Multistability and multiperiodicity of delayed Cohen-Grossberg neural networks with a general class of activation functions

In this paper, by using analysis approach and decomposition of state space, the multistability and multiperiodicity issues are discussed for Cohen-Grossberg neural networks (CGNNs) with time-varying delays and a general class of activation functions, where the general class of activation functions consist of nondecreasing functions with saturation’s including piecewise linear functions with two corner points and standard activation functions as its special case. Based on the Cauchy convergence principle, some sufficient conditions are obtained for checking the existence and uniqueness of equilibrium points of the n-neuron CGNNs. It is shown that the n-neuron CGNNs can have 2ⁿ locally exponentially stable equilibrium points located in saturation regions. Also, some conditions are derived for ascertaining equilibrium points to be locally exponentially stable or globally exponentially attractive and to be located in any designated region. As an extension of multistability, some similar results are presented for ascertaining multiple periodic orbits when external inputs of the n-neuron CGNNs are periodic. Finally, three examples are given to illustrate the effectiveness of the obtained results.     

Stochastic stabilization of hybrid neural networks by periodically intermittent control based on discrete-time state observations

This paper is concerned with stabilization of hybrid neural networks by intermittent control based on continuous or discrete-time state observations. By means of exponential martingale inequality and the ergodic property of the Markov chain, we establish a sufficient stability criterion on hybrid neural networks by intermittent control based on continuous-time state observations. Meantime, by M-matrix theory and comparison method, we show that hybrid neural networks can be stabilized by intermittent control based on discrete-time state observations. Finally, two examples are presented to illustrate our theory.     

A deep neural network-based method for solving obstacle problems

In this paper, we propose a method based on deep neural networks to solve obstacle problems. By introducing penalty terms, we reformulate the obstacle problem as a minimization optimization problem and utilize a deep neural network to approximate its solution. The convergence analysis is established by decomposing the error into three parts: approximation error, statistical error and optimization error. The approximate error is bounded by the depth and width of the network, the statistical error is estimated by the number of samples, and the optimization error is reflected in the empirical loss term. Due to its unsupervised and meshless advantages, the proposed method has wide applicability. Numerical experiments illustrate the effectiveness and robustness of the proposed method and verify the theoretical proof.     

双向中继网络中基于压缩感知的稀疏信道估计

为提高双向中继网络中稀疏信道估计的精度并减少训练序列的长度, 利用双向中继信道(Two-way Relay Channel, TWRC)的潜在稀疏特性, 研究了基于压缩感知的稀疏TWRC估计问题, 提出了一种改进的正交匹配追踪(Improved Orthogonal Matching Pursuit, IOMP)算法. 新算法运用迭代重加权最小二乘估计代替了正交匹配追踪(Orthogonal Matching Pursuit, OMP)算法中的最小二乘估计过程, 通过对样本进行迭代重加权, 逐步减小了异常样本的影响, 不断地修正了估计值, 在使用相同长度的训练序列时, 提高了估计的精度. 与传统的最小二乘估计算法相比, 新算法能够在获得相同估计效果的情况下, 显著减少所需训练序列的长度. 仿真结果验证了基于新算法的稀疏TWRC估计的有效性.     

The correlation length of commodity markets 1. Empirical evidence

It is a common belief nowadays that the world economy is fairly well “integrated”. Yet, this belief often turns out to be in contradiction with empirical evidence. As a matter of fact the way distant markets interact is a question that has largely been ignored by economists. In this series of two papers we examine the role that space, that is to say geographical distance, plays in the economics of commodity markets. The first of these papers presents the empirical evidence while the second develops a theoretical framework. The empirical enquiry discloses several noteworthy features, e.g. (i) with respect to spatial interaction there is a sharp contrast between stock markets and commodity markets. While there is almost perfect spatial arbitrage in the first case, this is not true for commodity markets. (ii) In spite of their chaotic behavior in the course of time commodity prices display well defined spatial patterns, (iii) as in statistical physics and fluid dynamics interactions can be described in terms of correlation length. The correlation length of a set of markets is seen to increase along with the number of transactions; it also increases when transport costs decline as was the case during the “transportation revolution” of the mid-nineteenth century. Using the notion of correlation length one is able to give a quantitative meaning to the otherwise ill-defined concept of market integration. Received 17 May 1999 and Received in final form 31 May 1999