Fuzzy adaptive predictive flow control of ATM network traffic

In order to exploit the nonlinear time-varying property of network traffic, the traffic flow from controlled sources is described by a fuzzy autoregressive moving-average model with auxiliary input (fuzzy ARMAX process), with the traffic flow from uncontrolled sources (i.e., cross traffic) being described as external disturbances. In order to overcome the difficulty of the transmission delay in the design of congestion control, the fuzzy traffic model is translated to an equivalent fuzzy predictive traffic model. A fuzzy adaptive flow control scheme is proposed to avoid congestion at high utilization while maintaining good quality of service. By use of fuzzy adaptive prediction technique, the difficulties in congestion control design due to nonlinearity, time-varying characteristics, and large propagation delay can be overcome by the proposed adaptive traffic control method. A comparative evaluation is also given to show the superiority of the proposed method.     

高速公路实时交通流模糊逻辑优化控制系统设计

高速公路交通流特征复杂多变,受到诸多影响因素的共同作用,如天气、道路状况、车辆类型等等。因此,如何获取交通流逻辑控制条件并实现精准调节难度较大。为此,本研究设计新的高速公路实时交通流模糊逻辑优化控制系统。对于系统的硬件部分,设计车流控制模块、无线传输车载终端、实时交通流逻辑调度模块、模糊控制器模块,并确定各个模块之间实时连接关系。对于系统的软件部分,求解交通流逻辑控制条件,联合高速公路实时通行相位条件,调节模糊控制器,并根据高速公路交通路网模型,推导交通流优化控制条件的求解表达式,完成高速公路实时交通流模糊逻辑优化控制系统的设计。实验结果表明,本系统可以解决逻辑协调错误的问题,且能够有效控制高速公路车流量。     

城市过饱和路网的偏好多目标相容优化控制

针对城市过饱和路网的交通信号控制问题,提出将总延误分为主线路段延误和次线路段延误的划分方法,将控制问题描述为冲突的多目标控制。针对冲突多目标控制问题中最优解不唯一,而传统优化方法中一次运行只能得到一个最优解的问题,提出了一种基于偏好的相容优化控制算法：利用偏好信息动态指引寻优的方向,在偏好区域内获得更多有价值的解;提出基于偏好的目标选择函数,保证控制解在偏好区域内的稳定性。在仿真环境中对一个11个交叉口的城市过饱和路网进行实时控制,结果表明,提出的偏好相容控制算法的效果优于定时控制方案。     

基于粒子群优化的神经网络短时交通流量预测

基于短时交通量的不确定特性,对城市相邻交叉口路段的交通流建模方法进行了研究.提出了基于粒子群优化的BP神经网络的信号交叉口交通量预测模型.该模型以BP神经网络为基础,用PSO算法对BP神经网络权值和阚值进行优化,从而提高了网络的预测精度.实时预测时,不只考虑本路口前几个时段交通量,同时也考虑了上下游路段的交通量的影响.仿真结果表明,粒子群-BP神经网络预测模型可以成为交通量预测的一种有效手段.     

基于多维业务的网络代价分布式优化算法

实际的通信网络是由多种网络组成的,网络代价最小化问题需要研究各个网络应当承载的业务量,然而目前网络负载均衡算法对业务分配优化问题仅考虑单网络场景.针对多网络场景,提出了四种不同网络间相互合作的业务分配问题的多维业务分配算法.该算法在考虑网络间的业务分配问题时,从网络容量、业务类型、网络覆盖范围、用户习惯和终端等几个因素综合考虑,在这些因素的约束下,通过求解网络代价最小化方程,得到了最优业务分配方案.通过对单位区域内的多网络负载场景进行仿真,验证了该算法对网络负载优化的有效性,可充分降低网络负载,适用性强.     

利用流控设备管理和优化校园网流量

由于P2P技术迅猛发展,中国药科大学校园网出口拥塞严重,迅雷和网络视频占用了大量带宽,导致校园网用户的正常应用无法保障。为了解决此问题,中国药科大学在校园网出口部署了流控设备,对出口流量进行优化和管理,有效的检测和防止非关键应用对网络带宽资源的大量消耗,保证关键应用的带宽使用,校园网出口流量得到了有效的管理和控制。     

Dynamic network flow model for short-term air traffic flow management

Since it is safer and less expensive to absorb delays on the ground, many models have been developed to assign ground holding delays optimally in the general network of airports, so that the total (ground plus airborne) delay costs of all fights are minimized. Still, the undeniable fact remains that airborne delays cannot be totally avoided. When there are airborne holds, such as when the local airspace capacity has a sudden decrease due to bad weather or other unpredicted disruptions, it is important to quickly minimize the costs of airborne holds. Much research has been conducted to explore ways to minimize airborne holds. Motivated by this, after analyzing the network structure of air traffic control areas and the dynamic nature of air traffic flow, this paper presents a model based on multicommodity dynamic network flow for short-term air traffic flow management. The model was validated by practical data from the Beijing ATC Center of the Civil Aviation Administration of China (CAAC).     

高速公路交通的神经网络自适应控制

建立宏观的高速公路交通流动态模型并设计理想的控制方案具有重要的理论意义和实际应用价值.在高速公路交通流宏观模型中含有复杂的非线性关系,一般的最优控制方案在计算上存在维数灾难问题,而且控制目标函数的设计往往有失偏颇.为此,本文建立了一个包括神经网络结构在内的宏观高速公路交通流动态模型,从高速公路管理者和使用者两方面的需求出发,设计了能够反映高速公路总体运营水平的目标函数,并提出相应的自适应控制方案.该方案结构简单、计算方便.数值仿真实验的结果表明控制效果良好,与不施加控制的运营方式相比较能够提高高速公路交通的总体运营水平.     

基于多学科设计优化的路网交通分布式协同控制

城市路网交通控制直接影响着交通运行效率,对其优化研究已成为缓解城市交通拥堵问题的热点之一.鉴于此,针对高峰交通路网将其分为过饱和区域与过饱和关联区域,在采用灰色关联分析-谱聚类方法对关联区域划分的基础上,构建路网交通分布式协同控制模型,进一步提出基于多学科设计优化的过饱和区域及其关联区域协同优化求解方法.通过搭建实例路网模型分析算法优化效果,结果表明所提出方法能够明显改善路网交通运行效率,有助于缓解城市通勤高峰时段的交通拥堵和扩散问题.     

区域交通信号系统节点分析及优化策略研究

SCOOT、SCATS等区域交通信号控制系统的关键路口通常根据交叉口交通流量、道路联接数等指标来确定,很难保证“蓄意”交通事件下区域网络运行的有效性。鉴于城市交通网络的无标度特性,以路段阻抗为权重将城市交通网络抽象为复杂加权网络,提出以网络凝聚度为指标的加权节点收缩方法,评估交叉口节点的重要性,解决区域交通网络hub点的选择问题,并用实例说明该方法的有效性,进而以SCATS系统为对象提出基于hub点选择的系统优化策略。     

基于网络流的嵌入式互联网中流量控制与优化

针对嵌入式互联网中实时应用的受限问题,本文提出优化网络资源,平衡网络流量的解决问题思路.基于此,采用图论中的最大流最小割定理,在最小物理网络拓扑结构中构建流量控制模型,并提出一种动态离散粒子群优化算法(DDPSO). DDPSO通过优化模型中的最大流函数继而优化控制网络流量.仿真实验证明:本文提出的DDPSO算法在定义域内具有较好的收敛性;当采用分级优化策略优化控制网络流量时,本文提出的优化策略可以有效优化网络流量,充分利用网络资源,以保证实时应用要求.     

Ping-pong flow control for ATM ABR traffic

In this paper, we propose an improved technique for congestion control, named as ping-pong flow control (PPFC), for asynchronous transfer mode (ATM) available bit rate (ABR) traffic. This is a rate-based flow control scheme, in which the rate regulation is achieved by directly adjusting the transmission rate in the source end station. The proposed algorithm uses a bipolar feedback strategy, which employs positive and negative feedbacks to control the transmission rate for different switch states. These states are determined using the traditional threshold-based method. We also introduce state early detection (SED), which enables the PPFC to control traffic flows more precisely and accurately at critical moments. The simulation results show that the proposed algorithm provides a higher throughput and lower cell loss ratio when compared to the well-known backward explicit congestion notification (BECN). Furthermore, these results also show that PPFC is robust against feedback losses.     

Multi-criterion system optimization model for urban multimodal traffic network

In this paper,computational models of environmental pollution and energy consumption of urban multimodal traffic network are proposed according to pertinent research and a multi-objective programming model is then developed to formulate optimization problem for such a system.Simultaneously,the main factors,such as travel time,pricing and convenience which influence travelers’ choice behaviors are all considered and a combined assignment model is proposed to simulate travelers’ mode and route choices.A bi-le...     

基于模糊控制算法的城市交通区域协调控制

针对目前我国城市交通区域控制的研究现状,应用群决策理论和模糊控制理论,对整个城市区域交通建立协调二级模糊控制模型。以相邻交叉口之间的车流量信息和预测剩余时间周期为模糊控制器输入,由二级模糊控制器的输出控制下一时刻的绿灯相位,从而调整控制子区内相邻交叉口上下游之间的交通流和集散程度,最终实现对控制子区的宏观区域协调控制。     

Optimization of robust area traffic control with equilibrium flow under demand uncertainty

For area traffic control road network under realization of uncertain travel demand, a robust signal setting is investigated in this paper. Due to certain hierarchy in a decision-making order, a min–max bilevel program is proposed. A new solution method is presented to determine a Nash–Stackelberg solution where a proposed signal setting is found for area traffic control under demand uncertainty. In order to investigate the robustness of the proposed signal settings, numerical computations are performed for various initial data sets in a medium-sized example road network. Good computational results indicated that the proposed signal settings can successfully reduce a worst-case travel cost substantially while incurring a relatively slight loss of optimality with respect to the optimal deterministic solutions for nominal travel demands. Particularly, our computation results showed that the proposed signal settings become even attractive as demand growth increases under a worst-case realization taken by uncertain travel demands.     

The air traffic flow control problem as an application of network theory

In this paper the air traffic flow control is approached as a constrained optimization problem on a multicommodity network. The proposed dynamic mathematical model, by means of a suitable time discretization, has been changed into a “static” one, in order to use static network flow algorithms while taking into account the “unsteady” nature of the air traffic congestion problems.The complexity of the model requires some preliminary effort, such as the identification of some characteristic parameters of the system.In this paper, the network theory is applied to evaluate the influence of the time discretization interval on the model significancy with respect to the actual traffic situation. In particular, a computational example concerning the Rome air traffic control region is presented and the relative results are discussed.     

认知无线电网络中接入控制和功率波束形成的联合优化

在认知无线电网络中,针对鲁棒性的多用户接入控制和发射功率波束形成的联合优化问题,提出了基于熵函数光滑近似的联合优化方案。首先,利用L₀-范数最小化将接入控制和发射功率波束两个优化问题转化为一个联合优化问题;然后,利用基于熵函数光滑近似的方法对L₀-范数的非凸性及不连续性问题加以优化;最后,由于光滑可微的目标函数为单峰函数,将问题变形为增广Lagrange函数,利用Armijo梯度下降法得到问题的最优解。数值结果分析表明：新算法在信干噪比（SINR）较低时虽然所提算法的接入量无明显提高,但是在SINR较高时所提算法能显著降低发射功率并提高次用户的接入量。模型中对不确定的信道状态信息（CSI）加以分析,可以使网络更好地适应外界的变化,提高网络的可靠性,所提算法可以有效地实现网络资源的优化配置,提高网络性能。     

Joint optimization of hardware and network systems

Multiple combinations of hardware and network components can be selected to design an information technology (IT) infrastructure that satisfies requirements. The professional criterion to deal with these degrees of freedom is cost minimization. However, a scientific approach has been rarely applied to cost minimization, especially for the joint optimization of hardware and network systems. This paper provides an algorithm combining hardware and network design issues in a single cost-minimization problem. The algorithm is empirically verified with a database of costs that has also been built as part of this research. Results show how a joint cost-minimization approach provides significant cost reductions with respect to the separate optimization of hardware and network sub systems. This indicates that the generally adopted divide et impera professional principle can be cost inefficient with respect to a rigorous scientific approach. Sensitivity and scalability analyses show that the algorithm's results are also robust and do not involve cost disadvantages over time.     

改进PSO优化神经网络的短时交通流预测

在短时交通流预测中,传统PSO优化神经网络预测模型对逃逸粒子直接取边界值且自身无相应的变异机制,这对于维持粒子群多样性、寻找最优解是不利的。为更进一步提高短时交通流预测精度,将在传统PSO优化BP神经网络的基础上,引入边界变异算子、自变异算子对粒子进行双重变异以优化网络配置参数。用实测的北京二环交通流数据对改进的预测模型进行验证,结果表明该模型更有利于搜寻全局最优解,且寻优时间更短,能有效改善短时交通流预测性能。