Fixed-Time Consensus-Based Nash Equilibrium Seeking

<正>Dear Editor, This letter examines the fixed-time stability of the Nash equilibrium(NE) in non-cooperative games. We propose a consensus-based NE seeking algorithm for situations where players do not have perfect information and communicate via a topology graph. The proposed algorithm can achieve NE in a fixed time that does not depend on initial conditions and can be adjusted in advance. In this strategy,     

Modeling and Analysis of Mesh Tree Hybrid Power/Ground Networks with Multiple Voltage Supply in Time Domain

This paper proposes a novel algorithm, which can be used to model and analyze mesh tree hybrid power/ground distribution networks with multiple voltage supply in time domain. Not only this algorithm enhances common method's ability on analysis of power/ground network with irregular topology, but also very high accuracy it keeps. The accuracy and stability of this algorithm is proved using strict math method in this paper. Also, the usage of both precondition technique based on Incomplete Choleskey Decomposition and fast variable elimination technique has improved the algorithm's efficiency a lot. Experimental results show that it can finish the analysis of power/ground network with enormons size within very short time. Also, this algorithm can be applied to analyze the clock network, bus network, and signal network without buffer under high working frequency because of the independence of the topology.     

Second Attribute Algorithm Based on Tree Expression

One view of finding a personalized solution of reduct in an information system is grounded on the viewpoint that attribute order can serve as a kind of semantic representation of user requirements. Thus the problem of finding personalized solutions can be transformed into computing the reduct on an attribute order. The second attribute theorem describes the relationship between the set of attribute orders and the set of reducts, and can be used to transform the problem of searching solutions to meet user requirements into the problem of modifying reduct based on a given attribute order. An algorithm is implied based on the second attribute theorem, with computation on the discernibility matrix. Its time complexity is O（n^2 × m） （n is the number of the objects and m the number of the attributes of an information system）. This paper presents another effective second attribute algorithm for facilitating the use of the second attribute theorem, with computation on the tree expression of an information system. The time complexity of the new algorithm is linear in n. This algorithm is proved to be equivalent to the algorithm on the discernibility matrix.     

Studies on algorithms for self-stabilizing communication protocols

In this paper the algorithms for self-stabilizing communication protocols are studied.First some concepts and a formal method for describing the proposed algorithms are described,then an improved algorithm for achieving global states is presented.The study shows that the improved algorithm can be applied to obtain the global states in the case of a loss of cooperation of the different processes in th protocol,which can be used as a recovery point that will be used by the following recovery procdure.Thus,the improved algorithm can be used to self-stabilize a communication protocol.Meanwhile,a recovery algorithm for selastabilizing communication protocols is presented.After a failure is detected,all processes can eventually know the error.The recovery algorithm uses the contextual information exchanged during the progress of the protocol and recorded on the stable memory.The proof of correctness and analysis of complexity for these algorithms have been made.The availability and efficiency of the algorithms have been verified by illustrating the example protocols.Finally,some conclusions and remarks are given.     

An Adaptive Learning Method for the Generation of Fuzzy Inference System from Data

Designing a fuzzy inference system （FIS） from data can be divided into two main phases： structure identification and parameter optimization. First, starting from a simple initial topology, the membership functions and system rules are defined as specific structures. Second, to speed up the convergence of the learning algorithm and lighten the oscillation, an improved descent method for FIS generation is developed. Furthermore, the convergence and the oscillation of the algorithm are systematically analyzed. Third, using the information obtained from the previous phase, it can be decided in which region of the input space the density of fuzzy rules should be enhanced and for which variable the number of fuzzy sets that used to partition the domain must be increased. Consequently, this produces a new and more appropriate structure. Finally, the proposed method is applied to the problem of nonlinear function approximation.     

A Traffic Partition Algorithm for Switched LANs and Its Performance Analysis

An algorithm is proposed which can be used for the topology design of switched LAN with heavy traffic and multi-segments.The main principle of the algorithm is to split the whole traffic to segments as even as possible.The algorithm consists of binary division and ordinary division.When the number of segments equals to powers of 2,binary division is used;ordinary division is based on binary division but suitable to more common cases.Both correctness and time complexity of the algorithm are discussed in detail,and a comparison of the algorithm with the best result is given at the same time.     

Topology-Based Recommendation of Users in Micro-Blogging Communities

Nowadays,more and more users share real-time news and information in micro-blogging communities such as Twitter,Tumblr or Plurk.In these sites,information is shared via a followers/followees social network structure in which a follower will receive all the micro-blogs from the users he/she follows,named followees.With the increasing number of registered users in this kind of sites,finding relevant and reliable sources of information becomes essential.The reduced number of characters present in micro-posts along with the informal language commonly used in these sites make it difficult to apply standard content-based approaches to the problem of user recommendation.To address this problem,we propose an algorithm for recommending relevant users that explores the topology of the network considering different factors that allow us to identify users that can be considered good information sources.Experimental evaluation conducted with a group of users is reported,demonstrating the potential of the approach.     

基于鸽群的鲁棒强化学习算法

Reinforcement learning(RL) is an artificial intelligence algorithm with the advantages of clear calculation logic and easy expansion of the model. Through interacting with the environment and maximizing value functions on the premise of obtaining little or no prior information, RL can optimize the performance of strategies and effectively reduce the complexity caused by physical models . The RL algorithm based on strategy gradient has been successfully applied in many fields such as intelligent image recognition, robot control and path planning for automatic driving. However, the highly sampling-dependent characteristics of RL determine that the training process needs a large number of samples to converge, and the accuracy of decision making is easily affected by slight interference that does not match with the simulation environment. Especially when RL is applied to the control field, it is difficult to prove the stability of the algorithm because the convergence of the algorithm cannot be guaranteed. Considering that swarm intelligence algorithm can solve complex problems through group cooperation and has the characteristics of self-organization and strong stability, it is an effective way to be used for improving the stability of RL model. The pigeon-inspired optimization algorithm in swarm intelligence was combined to improve RL based on strategy gradient. A RL algorithm based on pigeon-inspired optimization was proposed to solve the strategy gradient in order to maximize long-term future rewards. Adaptive function of pigeon-inspired optimization algorithm and RL were combined to estimate the advantages and disadvantages of strategies, avoid solving into an infinite loop, and improve the stability of the algorithm. A nonlinear two-wheel inverted pendulum robot control system was selected for simulation verification. The simulation results show that the RL algorithm based on pigeon-inspired optimization can improve the robustness of the system, reduce the computational cost, and reduce the algorithm’s dependence on the sample database. © 2022, Beijing Xintong Media Co., Ltd.. All rights reserved.     

A learning particle swarm optimization algorithm for odor source localization

This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.     

基于生成树算法的链路层拓扑发现研究

随着大规模交换网络的发展,网络拓扑发现的研究由网络层拓展到数据链路层。链路层的拓扑发现能够发现网络层拓扑发现无法发现的局域网内部的详细的物理连接情况,对网络配置管理具有重要意义。研究了目前基于地址转发表（AFT）的方法,针对现有算法的不足作了一定分析,提出了一种基于生成树算法（STA）的链路层网络拓扑发现算法,利用SNMP获得网桥MIB中的生成树信息,通过分析这些信息计算出链路层的网络拓扑。该算法相比其它算法更简单、高效,有应用价值。     

基于VLAN的网络拓扑发现算法

随着网络技术的发展，虚拟局域网（VLAN）在网络中的应用越来越广。为了更加有效地对VLAN进行管理，本文提出了一种基于VLAN的网络拓扑发现算法，该算法基于生成树协议，利用SNI旧获取交换机舳中的生成树信息，来推导出每一个VLAN的网络拓扑结构。     

基于生成树协议的VLAN管理

随着网络技术的发展,虚拟局域网(VLAN)在网络中的应用越来越广。为了更加有效的对VLAN进行管理,本文提出了一种基于VLAN的网络拓扑发现算法,该算法基于生成树协议,通过获取每VLAN中的生成树信息,来推导出每VLAN的网络拓扑结构,从而达到管理的目的。     

基于SNMP的物理网络拓扑发现的实现

网络拓扑信息对于网络管理是至关重要的。使用简单网络管理协议SNMP可以获取存放在网络设备的管理信息库MIB中的信息。根据MIB中的地址转发表,判断各网络设备之间的端口链接,从而可以得到局域网的网络拓扑结构。实验表明,该过程有效、可用,能够准确发现局域网的拓扑结构。     

基于SNMP的以太网拓扑发现

基于SNMP的物理拓扑发现为通用网管平台提供了物理拓扑发现功能。分析比较基于网桥转发表和基于网桥生成树的拓扑发现算法，结合两种算法的优点，提出一种新的以太网拓扑发现算法，该算法简单高效而且适用范围广；最后给出算法的实现与测试结果。     

网络拓扑搜索技术研究

首先描述了几类集中式网络拓扑自动搜索技术,它们分别是利用SNMP协议访问MIB（Management Information Base)构造网络拓扑、基于OSPF协议的网络拓扑搜索,基于RIP协议的网络拓扑搜索以及其它一些网络拓扑搜索技术,然后对利用SNMP协议访问MIB构造网络拓扑图的几种实现技术进行了一些详细的分析,并对它们进行了一些比较,最后提出了一种分布式网络拓扑搜索技术的实现模型。     

基于SNMP的链路层拓扑发现算法

提出一种基于地址转发表的网络拓扑发现算法,利用简单网络管理协议获得网桥MIB中的地址转发表信息,从而推导出连接关系。与现有方法相比,它不要求各个网桥转发表的信息是完备的,也无须进行大量比较。仿真实验结果表明,该算法能够准确地计算出整个被管网络的二层拓扑结构。     

物理网络拓扑发现算法的研究

局域网交换技术提高了局域网内网格带宽使用的有效性并且部分解决了现有网络带宽问题，在对原有基于SNMP MIB－II的物理网络设备发现算法分析比较的基础上，提出了一种新的算法，用该算法开发的拓扑发现工具能准确地发现物理网络拓扑即子网内交换机到交换机，交换机到路由器，交换机到主机之间的连接关系。     

基于生成树协议的VLAN管理

随着网络技术的发展，虚拟局域网（VLAN）在网络中的应用越来越广。为了更加有效的对VLAN进行管理，本文提出了一种基于、VLAN的网络拓扑发现算法，该算法基于生成树协议，通过获取每VLAN中的生成树信息．来推导出每VLAN的网络拓扑结构，从而达到管理的目的。     

一种改进的网络拓扑发现方法

在对基于ICMP的网络拓扑发现、基于ARP的网络拓扑发现和利用SNMP访问MIB(管 理信息库)路由表的网络拓扑发现三种方法的分析研究基础上,提出了一个经过改进的网络拓扑发 现方法,此方法能够准确、完整、高效地发现网络主干拓扑和子网内的设备,并详细描述了网络拓扑发 现的数据结构和算法。