Accelerating a Recurrent Neural Network to Finite-Time Convergence for Solving Time-Varying Sylvester Equation by Using a Sign-Bi-power Activation Function

Bartels–Stewart algorithm is an effective and widely used method with an O(n ³) time complexity for solving a static Sylvester equation. When applied to time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. Gradient-based recurrent neural network are able to solve the time-varying Sylvester equation in real time but there always exists an estimation error. In contrast, the recently proposed Zhang neural network has been proven to converge to the solution of the Sylvester equation ideally when time goes to infinity. However, this neural network with the suggested activation functions never converges to the desired value in finite time, which may limit its applications in realtime processing. To tackle this problem, a sign-bi-power activation function is proposed in this paper to accelerate Zhang neural network to finite-time convergence. The global convergence and finite-time convergence property are proven in theory. The upper bound of the convergence time is derived analytically. Simulations are performed to evaluate the performance of the neural network with the proposed activation function. In addition, the proposed strategy is applied to online calculating the pseudo-inverse of a matrix and nonlinear control of an inverted pendulum system. Both theoretical analysis and numerical simulations validate the effectiveness of proposed activation function.     

Effect of Ce(3+) on soluble microbial products production in anaerobic granular sludge digestion

Upflow anaerobic sludge bed reactors fed on glucose were used to investigate the effects of Ce(3+) on soluble microbial products (SMP) production, which is the majority of the residual chemical oxygen demand present in the effluent. It was found that Ce(3+) concentration of 0.05 mg/L had no significant effect on the amount of SMP production, whereas that of 1 mg/L led to the increase in SMP production. The molecular-weight distribution and carbohydrate analysis indicated that an increase in SMP production may be partly attributed to the release of extracellular polymeric substances (EPS) into the bulk solution resulted from cerium toxicity, and the nucleic acids analysis suggested that increased cell lysis also contributed to SMP accumulation in the presence of Ce(3+). The increase in SMP production in the presence of Ce(3+) is possibly a consequence of the release of EPS and increased cell lysis due to cerium toxicity.     

开关跳闸的原因及对策

分析日常生产及供电系统中,空气开关(断路器)跳闸的可能原因,并结合现场实际情况给出相应的处理方法与对策.     

网络设备配置信息备份系统的设计与实现

提出了如何设计开发网络设备配置信息备份系统,实现各种网络设备配置信息的远程备份,并用VBScript语言进行了具体实现。     

Google体系结构分析

为了适应大规模广范围高密度的搜索,Google的网络搜索应用允许在不同的处理器上运行不同的查询;同时,通过对全局索引进行分解,Google也允许在多个处理器上运行单一的查询.Google的主旨是,使用市场上流行的具有通用的PC,以达到大型机所具有的高性能.通过对已有资料进行搜集,对Google的体系结构、工作性能等进行分析,目的是通过对Google的调研,给网格建设提供一定的借鉴.     

通信企业计算机网络安全技术研究

文章主要阐述了计算机网络攻击和入侵的特点、方法以及其安全防范手段,并集中对目前计算机网络安全技术进行了研究.     

Locally linear discriminant embedding: An efficient method for face recognition

In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.     

Small sample improvements in the threshold cointegration test using residual-based moving block bootstrap

A residual-based moving block bootstrap procedure for testing the null hypothesis of linear cointegration versus cointegration with threshold effects is proposed. When the regressors and errors of the models are serially and contemporaneously correlated, our test compares favourably with the Sup LM test proposed by Gonzalo and Pitarakis. Indeed, shortcomings of the former motivated the development of our test. The small sample performance of the bootstrap test is investigated by Monte Carlo simulations, and the results show that the test performs better than the Sup LM test.     

GML模式匹配技术研究

GML是基于XML的空间信息编码标准。GML模式匹配技术作为实现应用模式的集成与共享的关键技术,与GML解析机理和实现技术密切相关。本文详细介绍了关于GML模式匹配的概念;给出了匹配方法的步骤、算法及已所实现的模式匹配详细流程,包括语意与结构相似度判断以及权重的设置;最后,对匹配过程中包含的不确定因素进行了分析与讨论。     

基于主次原型超平面最接近支持向量机

基于广义特征值的最接近支持向量机GEPSVM是一种新的具有与SVM性能相当的两类分类方法,通过求解广义特征值来获得两个彼此不平行的拟合两类样本的超平面,其决策规则是将测试样本归为距其最近的超平面所在的类。然而,该规则在某些情形会导致较差的分类结果。对此,本文提出了在利用GEPSVM产生一个主原型超平面的基础上,再利用主原型超平面及它类样本的信息构造一个次原型超平面,形成一个由主次原型超平面共同决策的最接近支持向量机。该方法不仅简单且易于实现,而且具有较GEPSVM更优的分类性能。在UCI数据集上的实验验证了它的有效性。