不确定未知界的相似组合系统分散自适应输出反馈控制

研究一类具有加性振动的不确定相似组合大系统的分散自适应输出反馈镇定问题,给出了该系统输出反馈镇定的条件。受控系统的不确定项可以是非线性的或时变的,输入通道不确定性满足一般限制,系统的不确定项的界是存在的但却是未知的。所设计的确定性非线性控制器能保证受控系统渐近收敛于系统的平衡点,并且自适应律保持有界。最后进行了仿真研究,表明所得结论是正确脯效的。     

连续混沌系统自适应同步控制器设计

本文设计了一种能实现连续时间混沌系统同步的自适应连续控制器．无论驱动系统 处于何种状态,在自适应连续控制器的控制下都可使响应系统按照驱动系统给定的轨道演化 ．该自适应连续控制器对系统参数不匹配及噪声干扰具有一定的鲁棒性．最后以蔡氏电路混 沌系统为例进行的数值仿真说明了本文提出方法的有效性．     

一种非线性和时滞系统自适应控制方法

提出一种新的基于高斯基函数(GPFN-G aussian potential function networks)网络的内模控制算法,并对高斯基函数网络内部模 型和内模控制器的建立进行了深入分析．仿真及实时控制结果表明该算法是有效的,具有很 好的自适应性和鲁棒性,可以应用于具有时滞和非线性时变系统中．     

Parallelization of a Hyper-Linking–Based Theorem Prover

We describe the parallelization of a first-order logic theorem prover that is based on the hyper-linking proof procedure (HLPP). Four parallel schemes – process level, clause level, literal level, and flow level – are developed for two types of sequential implementation of HLPP: list based and network based. The motivation for developing each parallel scheme is presented, and the architecture and implementation details of each scheme are described. Issues about parallel processing, such as serialization and synchronization, load balancing, and access conflicts, are examined. Speedups over sequential implementations are attained, and timing results for benchmark problems are provided.     

基于测量的接纳控制研究

与传统的接纳控制算法相比,基于测量的纳控制有诸多优点,首先它无需知识应用的流量模型,其次它能动态适应网络的负载变化,提高网络资源的利用率。文中分析了基于测量的接纳控制的基本思想,并在此基础上提出和实现了一种自适应的接纳控制算法（Adaptive Measurement-Based Admission Control,AMBAC).作者通过实验对该算法进行了验证,发现在系统资源利用率（或接纳能力）接近的情况下,与传统的（固定时间窗口的）,MBAC相比,AMBAC能达到更低的平均分组丢失率。     

基于自组织神经网络的烧结终点自适应预报系统的开发

烧结终点的在线检测和提前预报对于稳定终点,进而提高烧结矿强度和产量、降低能耗有重要意义。文章介绍了烧结终点的软测量方法;提出了一个新的预报参数——风箱废气温度曲线拐点;将多层前向人工神经网络应用于烧结终点的预报,对BP算法做了较大改进,使学习算法可以自组织神经网络的结构。应用这些技术开发的烧结终点自适应预报系统能够快速、准确地判断和预报烧结终点的状态,可用于在线操作指导或作为自动控制的依据。     

计算机网络管理综论

本文论述了实现现代计算机网络管理的相关原理及技术,并就现代网络管理的新特性及其相应的网络管理设计策略进行了分析、讨论。最后简要介绍了几个常见的网络管理软件产品。     

On-line Algorithm for Blind Signal Extraction of Arbitrarily Distributed,but Temporally Correlated Sources Using Second Order Statistics

Most of the algorithms for blind separation/extraction and independent component analysis (ICA) can not separate mixtures of sources with extremely low kurtosis or colored Gaussian sources. Moreover, to separate mixtures of super- and sub-Gaussian signals, it is necessary to use adaptive (time-variable) or switching nonlinearities which are controlled via computationally intensive measures, such as estimation of the sign of kurtosis of extracted signals. In this paper, we develop a very simple neural network model and an efficient on-line adaptive algorithm that sequentially extract temporally correlated sources with arbitrary distributions, including colored Gaussian sources and sources with extremely low values (or even zero) of kurtosis. The validity and performance of the algorithm have been confirmed by extensive computer simulation experiments.     

Towards the Optimal Learning Rate for Backpropagation

A backpropagation learning algorithm for feedforward neural networks withan adaptive learning rate is derived. The algorithm is based uponminimising the instantaneous output error and does not include anysimplifications encountered in the corresponding Least Mean Square (LMS)algorithms for linear adaptive filters. The backpropagation algorithmwith an adaptive learning rate, which is derived based upon the Taylorseries expansion of the instantaneous output error, is shown to exhibitbehaviour similar to that of the Normalised LMS (NLMS) algorithm. Indeed,the derived optimal adaptive learning rate of a neural network trainedby backpropagation degenerates to the learning rate of the NLMS for a linear activation function of a neuron. By continuity, the optimal adaptive learning rate for neural networks imposes additional stabilisationeffects to the traditional backpropagation learning algorithm.     

神经网络自适应噪声对消的研究

基于神经网络中自适应线性单元(ADALINE),结合传统自适应噪声对消原理,提出了一种基于ADALINE的改进型自适应噪声对消器。经仿真和实例证明,具有很强的噪声滤除能力和鲁棒性。