静态路由也“浮动”

动态路由因为需要路由器之间频繁地交换各自的路由表，而对路由表的分析可以揭示网络的拓扑结构和网络地址等信息。因此，网络出于安全方面的考虑也可以采用静态路由。本文详细描述了专线业务中浮动静态路由的使用方法，一起来学习下吧。     

基于网络模体的轻量级物联网拓扑优化策略研究

随着第五代通信网络技术(5G)的发展,智慧城市中物联网(Internet of Things,IoT)的应用规模和多样性呈现出爆炸式增长.海量的智能传感设备组网给高动态的物联网通信服务质量带来了巨大的威胁.部分关键设备节点的失效以及网络攻击易引发物联网的链锁崩塌效应,影响网络应用的服务质量.因此,如何优化大规模物联网拓扑的鲁棒能力成为当下的研究挑战.目前,针对物联网拓扑结构的优化问题,研究者们提出了启发式算法、智能学习机制和多目标优化策略等创新方法提高物联网拓扑结构的鲁棒能力.但是,这些方法需牺牲巨大的计算资源来获得不成比例的鲁棒性能增益,网络规模越大,该现象越明显.为了解决这个问题并平衡计算开销和提升鲁棒性能,本文提出了一种基于网络模体(Motif)的轻量级物联网拓扑优化策略LITOS.首先利用物联网拓扑结构的社区属性,设计一种基于网络模体的异步社区发现算法,将大规模复杂拓扑结构分解为轻量级局部网络拓扑.然后,基于CPU多核心的计算资源,设计深度强化学习机制,异步优化轻量级物联网局部拓扑结构,从而降低网络整体优化运行时间,提高拓扑结构鲁棒能力.在实验方面,与其他先进的优化算法相比,该...     

基于移动代理和动态拓扑结构的入侵检测系统模型

分布式网络攻击的破坏性越来越大。网络在运行中拓扑结构又是在动态变化的。如何在拓扑结构变化的网络中去发现和阻止网络攻击，本文提出了一个基于移动代理技术的模型。模型由拓扑发现代理、拓扑计算代理、检测代理、追踪代理、阻击代理组成。拓扑发现代理和拓扑计算代理完成网络拓扑结构跟踪，检测代理、追踪代理、阻击代理完成对分布式网络攻击的探测、追踪、阻止。该模型具有适合大规模网络、占用网络带宽少、能自动跟踪网络拓扑变化、系统的入侵检测和响应与拓扑变化无关等特点。     

基于正交多项式基的神经网络模型

本文采用一类正交多项式集合作为神经元的激励函数，构成一个正产多项式基神经网络。网络的拓扑结构２和相应的正交多项式基在学习的过程中确定，网络的权值经最小二乘算法得到，避免了局部极值问题，仿真结果表明，本文提出的方法是可行和有效的。     

分析无线传感器网络的拓扑控制

无线传感器网络的拓扑控制可以生成能量高效的数据转发网络拓扑结构。本文从无线传感器网络拓扑控制的重要性与设计目标出发,就拓扑控制算法等方面的内容进行了分析与探讨。     

构建安全的计算机网络技术实训室

计算机网络技术实训室是培养信息安全技术人才的平台。本文从建设需求、建设目标、建设内容等方面阐述了安全的计算机网络技术实训室建设,设计了网络技术实训的拓扑结构、设备清单和实训内容。     

手写体数字识别的一种模糊联想记忆神经网络方法

手写体数字识别是当前神经网络应用研究最为活跃的领域之一。本文找出一种基于模糊联想记忆神经网络模型的识别方法，并获得较为满意的计算机模拟结果。与常用的ＢＰ网络算法相比，具有学习速度快、算法简单、网络规模小、拓扑结构简单等优点。     

基于IP城域网的MPLS VPN拓扑结构分析

传统DDN专网属于星型拓扑结构，缺点是逻辑拓扑难于调整、网络可扩展性和可维护性较差。为了解决这些问题．在ISP的城域网中，利用MPLS技术组建VPN，通过LDP邻居测试、路由跟踪测试等方法和理论分枷，证明了MPLS VPN的逻辑拓扑结构为全网状，解决了传统覆盖型VPN拓扑结构不易调整的问题。     

基于密集连接卷积神经网络的链路预测模型

现有的基于网络表示学习的链路预测算法主要通过捕获网络节点的邻域拓扑信息构造特征向量来进行链路预测，该类算法通常只注重从网络节点的单一邻域拓扑结构中学习信息，而对多个网络节点在链路结构上的相似性方面研究不足。针对此问题，提出一种基于密集连接卷积神经网络（DenseNet）的链路预测模型（DenseNet-LP）。首先，利用基于网络表示学习算法node2vec生成节点表示向量，并利用该表示向量将网络节点的结构信息映射为三维特征数据；然后，利用密集连接卷积神经网络来捕捉链路结构的特征，并建立二分类模型实现链路预测。在四个公开的数据集上的实验结果表明，相较于网络表示学习算法，所提模型链路预测结果的ROC曲线下方面积（AUC）值最大提高了18个百分点。     

无线传感器网络带状拓扑路由研究与应用

本文围绕无线传感器网络在道路交通中的应用展开讨论，根据带状拓扑结构的特殊性，提出了一个分两级网络的路由协议，由上级节点发送路由请求建立路由，下级节点维护本地路由，并将下级网络按地理位置分成无簇头的分簇结构。应用实践表明，带状拓扑结构下，此路由协议简单，容易实现，开销小等。     

Pre-synaptic lateral inhibition provides a better architecture for self-organizing neural networks

Unsupervised learning is an important ability of the brain and of many artificial neural networks. A large variety of unsupervised learning algorithms have been proposed. This paper takes a different approach in considering the architecture of the neural network rather than the learning algorithm. It is shown that a self-organizing neural network architecture using pre-synaptic lateral inhibition enables a single learning algorithm to find distributed, local, and topological representations as appropriate to the structure of the input data received. It is argued that such an architecture not only has computational advantages but is a better model of cortical self-organization.     

一种改进的RBF网络结构及其参数的设计方法

针对神经网络在学习之后,模糊系统的原始结构被改变,或削弱了规则可解释性这一模糊系统突出特点的问题,给出了一种提取模糊If-then规则的径向基函数(RBF)神经网络结构。该神经网络结构具有能够同时清晰表达模糊控制系统输入空间划分和模糊规则可解释性的特点,克服了以往用神经网络提取模糊规则不能直观体现模糊语言规则可解释性的不足,并详细地讨论了此网络结构参数的设计方法。     

基于SOM实例归纳学习的信息系统总体设计模式生成方法

采用SOM神经网络技术通过对总体设计实例进行归纳学习,在信息系统开发的总体设计环节探索出一种挖掘和生成总体设计模式的方法,为模式在总体设计环节的应用奠定基础。它是对人工智能技术与信息系统开发相结合所进行的有益尝试。     

Functional-link net computing: theory, system architecture, andfunctionalities

A system architecture and a network computational approach compatible with the goal of devising a general-purpose artificial neural network computer are described. The functionalities of supervised learning and optimization are illustrated, and cluster analysis and associative recall are briefly mentioned     

Combining seasonal time series ARIMA method and neural networks with genetic algorithms for predicting the production value of the mechanical industry in Taiwan

Supplying industrial firms with an accurate method of forecasting the production value of the mechanical industry to facilitate decision makers in precise planning is highly desirable. Numerous methods, including the autoregressive integrated-moving average (ARIMA) model and artificial neural networks can make accurate forecasts based on historical data. The seasonal ARIMA (SARIMA) model and artificial neural networks can also handle data involving trends and seasonality. Although neural networks can make predictions, deciding the most appropriate input data, network structure and learning parameters are difficult. Therefore, this article presents a hybrid forecasting method that combines the SARIMA model and neural networks with genetic algorithms. Analytical results generated by the SARIMA model are inputted as the input data of a neural network. Subsequently, the number of neurons in the hidden layer and the number of learning parameters of the neural network architecture are globally optimized using genetic algorithms. This model is subsequently adopted to forecast seasonal time series data of the production value of the mechanical industry in Taiwan. The results presented here provide a valuable reference for decision makers in industry.     

基于进化计算的神经网络设计与实现

基于进化算法可有产解决神经网络设计和实现中存在的一些问题,使网络具有更优的性能。在此对基于进化计算的神经网络设计和实现的研究内容及进展情况进行综述,讲座了网络实现的关键问题,包括网络权重的进化训练,网络结构进化设计,学习规则进化选取以及进化操作算子设计等,并分析了相关的研究和发展方向。     

基于小波变换的肌电信号识别方法研究

针对肌电信号的非平稳特性,采用小波变换方法对表面肌电信号进行分析,提取小波系数最大值构造特征矢量输入神经网络分类器进行模式识别,经过训练能够成功地从掌长肌和肱桡肌采集的两道表面肌电信号中识别展拳、握拳、前臂内旋、前臂外旋四种运动模式。实验表明,基于小波变换的神经网络分类方法所需的数据短、运算快,对于肌电假肢的控制具有良好的应用前景。     

Face recognition Face2vec based on deep learning: Small database case

Object classification is a common problem in artificial intelligence and now it is usually approached by deep learning. In the paper the artificial neural network (ANN) architecture is considered. According to described ANN architecture, the ANN models are trained and tested on a relatively small Color-FERET facial image database under different conditions. The best fine-tuned ANN model provides 94% face recognition accuracy on Color-FERET frontal images and 98% face recognition accuracy within 3 attempts. However, for improving recognition system accuracy large data sets are still necessary preferably consisting of millions of images.     

A fast and efficient two-phase sequential learning algorithm for spatial architecture neural network

Spatial architecture neural network (SANN), which is inspired by the connecting mode of excitatory pyramidal neurons and inhibitory interneurons of neocortex, is a multilayer artificial neural network and has good learning accuracy and generalization ability when used in real applications. However, the backpropagation-based learning algorithm (named BP-SANN) may be time consumption and slow convergence. In this paper, a new fast and accurate two-phase sequential learning scheme for SANN is hereby introduced to guarantee the network performance. With this new learning approach (named SFSL-SANN), only the weights connecting to output neurons will be trained during the learning process. In the first phase, a least-squares method is applied to estimate the span-output-weight on the basis of the fixed randomly generated initialized weight values. The improved iterative learning algorithm is then used to learn the feedforward-output-weight in the second phase. Detailed effectiveness comparison of SFSL-SANN is done with BP-SANN and other popular neural network approaches on benchmark problems drawn from the classification, regression and time-series prediction applications. The results demonstrate that the SFSL-SANN is faster convergence and time-saving than BP-SANN, and produces better learning accuracy and generalization performance than other approaches.     

Evolutionary learning of fuzzy logic controllers and theiradaptation through perpetual evolution

This paper presents an adaptive control architecture, where evolutionary learning is applied for initial learning and real-time tuning of a fuzzy logic controller. The initial learning phase involves identification of an artificial neural network model of the process and subsequent development of a fuzzy controller with parameters obtained via a genetic search. The neural network model is utilized for evaluating trial fuzzy controllers during the genetic search. The proposed adaptive mechanism is based on the concept of perpetual evolution, where parameters of the fuzzy controller are updated at each time step with solutions extracted from a continuously evolving population of trials. There are two mechanisms that accommodate the real-time changes in the control task and/or the process into the continuous genetic search: a scheme that dynamically modifies the fitness evaluation criteria of the genetic algorithm, and an online learning of the neural network model used for evaluating the trial controllers. The potential of using evolutionary learning for real-time adaptive control is illustrated through computer simulations, where the proposed technique is applied to a chemical process control problem