基于自适应粒子群优化算法的交通量短时预测模型

在考虑交通量短时变化的时空特性和波动性基础上,建立了非线性交通量短时预测模型.根据我国城市道路交通流非线性、时变性、随机性等特点,提出自适应粒子群优化算法对非线性交通量短时预测模型进行在线修正.该自适应粒子群优化算法采用两步优化策略,对算法参数进行调整,避免算法早熟收敛,有效提高了算法的运算精度和效率.利用城市道路的实测数据,通过Mat-lab软件工具箱对该模型进行计算机仿真验证.     

Simple adaptive filtering scheme to improve measurement accuracy of gyroscope on angular motion base

The objective of this work is to improve the measurement accuracy of a gyroscope on a angular motion base with a simple adaptive filter scheme. Two main topics are highlighted in this work. The first topic is to show building a dual-process model employed for the conventional Kalman filter. The second topic is to show developing a modified noise adaptive algorithm when measurement noise and process noise are unknown. The experimental results are presented to show that the simple adaptive filtering scheme outperforms the other conventional scheme in this paper in terms of noise reduction.     

基于探测车数据的路段行程时间估计

利用探测车数据进行路段行程时间估计面临着两类误差：采样误差和非采样误差,从而导致估计结果精度不高和可靠性差。在回顾已有估计方法的基础上,有针对性地引入了自适应式卡尔曼滤波,建立了相应的状态方程和观测方程,利用相似时间特征的历史数据标定了状态转移系数,并对滤波进行了求解。以实际数据对估计方法进行了验证,平均相对误差为13.13%。研究表明,自适应式卡尔曼滤波能够应用到基于探测车数据的路段行程时间估计中来,并具有估计精度高、收敛速度快、参数少、对初值不敏感等优点。     

基于自适应Kalman滤波的交通流量监测算法

提出一种基于车载无线通信模块的交通流监测原型系统,该系统将行驶中载有无线通信模块的车辆视为移动检测节点,以很小的代价实现对交通信息的获取.在此基础上提出一个具体应用:利用基站对道路上载有通信模块车辆的检测,结合设计的基于结果反馈的自适应Kalman滤波算法,及时准确地完成对路段上交通流量的检测.此外,还基于元胞自动机理论搭建了仿真平台,并验证了算法的有效性.     

无速度传感器感应电机自适应观测器的稳定性分析与设计

分析了感应电机低速发电工况下现有采用极点配置的自适应观测器存在不稳定区域的原因,得出了自适应观测器在全速范围内稳定的条件.在此基础上提出一种改进的速度自适应律和观测器增益矩阵选取方案.应用Lyapunov稳定性理论,得到基于自适应观测器的速度辨识自适应律;观测器的增益矩阵通过求解2 个双线性矩阵不等式得到.在MATLAB/SIMULINK环境下,对基于自适应观测器的无速度传感器感应电机直接转矩控制进行了仿真.仿真结果表明,本文给出的自适应观测器在全速范围内具有良好的稳态和动态性能,并具有很好的鲁棒性.     

TRAFFIC-BASED SCHEDULING MECHANISM IN AN IP/ATM ROUTER

Introduction　　 In recentyears,ATM has been installed com-prehensively,especially in the backbone of cam-puses and cities. On account of the differencesamong ATM,IP and other networks[1,2 ] ,how toeffectively integrate them is a very important andcritical issue to ATM and other networks.An IP/ATM router which can interconnect1 55M ATM,1 0 0 M Ethernet,2 M ATM,PPP,2 B D ISDN and X.2 5is under developing in ourlab.In prior work,we have designed its softwarearchitecture[3] .We als…     

基于P1R3逻辑控制的车辆制动防抱死系统仿真研究

分析了车辆防抱死装置上广泛使用的P1R3逻辑控制算法,将这种逻辑门限控制算法与轮速计算中的精度自适应方法相结合,在不同路面附着系数下进行制动过程的动态仿真模拟,证实这种算法能满足防抱死制动控制的要求,尤其是在保证高速、低速的计算精度的同时,能有很好的低速控制实时性。     

无模型自适应控制算法在ROV定深控制中的仿真

将无模型自适应控制方法应用于ROV(Remote Operated Vehicle)定深控制当中。该控制方案的设计仅利用ROV的垂向推力输入数据和深度输出数据，用动态线性化时变模型替代ROV非线性系统模型，算法中不包含ROV模型及水动力参数信息。因此，解决了ROV因系统复杂，水动力参数难以确定所导致的控制器设计复杂度高，控制效果不理想的问题。为了便于仿真，本文建立含有补偿参数的ROV简化模型，模型仅用于产生系统的I/O数据，不参与控制器的设计。仿真结果表明，在ROV定深控制当中，无模型自适应控制(Model-free adaptive control，MFAC)比PID控制具有更强的抗扰能力。此外，在欠阻尼ROV系统中，基于偏格式动态线性化的无模型自适应控制(partial form dynamic linearization based Model-free adaptive control，PFDL-MFAC)方案相比于基于紧格式动态线性化的无模型自适应控制(compact form dynamic linearization based Model-free adaptive control，CFDL-MFAC)方案具有更好的控制效果。     

甚低频通信系统的自适应算术数据压缩研究

受其带宽的限制，甚低频通信系统的数据传输率较低。我们可通过自适应算术编码压缩来提高甚低频通信系统的数据传输率。为了提高算法的抗错误扩散能力，算法必须定期地返回到初始状态，然后采用纠错编码来降低错误率，但是这增加了冗余而牺牲了最终压缩效果。在考虑了开销之后，我们得出结论，自适应算术编码压缩技术确确实实能够为甚低频通信系统提高数据传输率。     

FAULT IDENTIFICATION IN HETEROGENEOUS NETWORKS USING TIME SERIES ANALYSIS

Fault management is crucial to provide quality of service grantees for the future networks, and fault identification is an essential part of it. A novel fault identification algorithm is proposed in this paper, which focuses on the anomaly detection of network traffic. Since the fault identification has been achieved using statistical information in management information base, the algorithm is compatible with the existing simple network management protocol framework. The network traffic time series is verified to be non-stationary. By fitting the adaptive autoregressive model, the series is transformed into a multidimensional vector. The training samples and identifiers are acquired from the network simulation. A k-nearest neighbor classifier identifies the system faults after being trained. The experiment results are consistent with the given fault scenarios, Which prove the accuracy of the algorithm. The identification errors are discussed to illustrate that the novel fault identification algorithm is adaptive in the fault scenarios with network traffic change.