多目标优化的新方法——幂加权和法及数值仿真

提出了一种新的评价函数,将多目标优化问题转化为单目标优化问题,并证明了由这种方法所得到的最优解是多目标优化问题的有效解.用混合罚函数法将约束问题变为无约束问题,将改进的模拟退火算法与单纯形法结合起来构造一种效率较高的全局优化算法,该算法具有模拟退火算法在全局搜索上的优点和单纯形法在局部搜索上的优点.用计算机语言编制了通用程序,给出了单目标优化和多目标优化的例子,数值仿真结果表明这种方法是可靠的.     

智能控制技术在汽车AMT中的应用

针对汽车AMT系统中节气门控制、离合器接合点及最佳动力性换档规律的确定等问题 ,讨论了采用多模式控制、模糊控制和自学习控制等智能控制的具体方法 .实际应用表明 ,这些方法切实可行 ,同时 ,也体现了智能控制技术在车辆控制系统中的实用性和先进性     

桥梁抗震分析中的激励输入模式研究

本文研究桥梁抗震分析中激励的输入模式。针对加速度、位移两种输入模式的特点、实现方法以及适用性进行对比分析。分析结果表明,若积分方法和步长相同,在有限元分析模型中加速度输入模式较位移输入模式容易获得更好的精度;而在模态分析模型中则得到相反的结论。为减小误差,模态模型中位移输入模式的模态力宜由等效分布荷载形式计算。实测地震加速度时程需要修正才能得到一致化的位移时程,在总结各类时域修正方法的基础上,本文提出一种频率内校正方法。     

Robust Predictive Control for Polytopic Uncertain Systems with Time-delay Subjected to Actuator Saturation

Robust predictive control algorithms were presented for polytopic uncertain linear discrete systems with time-delay subjected to actuator saturation. In the first algorithm, the parameter dependent state feedback model predictive control (MPC) law was obtained from minimizing the upper bound of the cost function subjected to several linear matrix inequality constraints. In order to reduce computation burden, a second robust MPC algorithm based on nominal performance cost was presented. The feasibility of the optimization problems guarantees that the algorithms are robustly stable. The simulation results verify the effectiveness of the proposed algorithms.     

基于模糊神经网络的Horn集上的输入归结

关于定理机器证明的归结原理已应用到人工智能的很多领域，同时提出了各种改进方法，其中，输入归结是一种非常好的推理方法，它对于Horn集是完备的，模糊神经网络是模糊逻辑与神经网络的融合，文中利用模糊神经网络的知识表示及学习的特点，结合输入归结的优点，进行Horn集上的输入归结。     

动态识别道路坡度的多传感器信息融合方法

在行驶中动态识别道路坡度的大小是采用电控机械自动变速器的车辆实现智能换挡控制的重要环节之一 ,也是目前的一大技术难题 .针对这一问题 ,提出了采用多个传感器信息融合来识别坡度角的方法 ,建立了数学模型 ,并对识别误差进行了分析 .     

基于椭圆曲线的广播型多重签名方案

多重数字签名的概念及方案的提出大大拓宽了数字签名的应用范围。基于椭圆曲线密码体制，提出了一种以椭圆曲线为基础的广播型多重数字签名方案。该方案研究了如何充分发挥椭圆曲线密码密钥长度短、效率高、安全强度大的优势来解决多重数字签名的缺陷，该方案可以有效地抵抗多种攻击，安全性高。     

Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

Intra‐city commuting is being revolutionized by call‐taxi services in many developing countries such as India. A customer requests a taxi via phone, and it arrives at the right time and at the right location for the pick‐up. This mode of intra‐city travel has become one of the most reliable and convenient modes of transportation for customers traveling for business and non‐business purposes. The increased number of vehicles on city roads and raising fuel costs has prompted a new type of transportation logistics problem of finding a fuel‐efficient and quickest path for a call‐taxi through a city road network, where the travel times are stochastic. The stochastic travel time of the road network is induced by obstacles such as the traffic signals and intersections. The delay and additional fuel consumption at each of these obstacles are calculated that are later imputed to the total travel time and fuel consumption of a path. A Monte‐Carlo simulation‐based approach is proposed to identify unique fuel‐efficient paths between two locations in a city road network where each obstacle has a delay distribution. A multi‐criteria score is then assigned to each unique path based on the probability that the path is fuel efficient, the average travel time of the path and the coefficient of variation of the travel times of the path. Copyright © 2016 John Wiley & Sons, Ltd.     

时空信息多模式融合模型应用研究

给出了时空多模式融合模型(MM模型)的实用建模方法.利用华北地震和北京SARS 2个时空数据集对MM模型进行了建模应用和性能评价,与单纯神经网络模型相比,MM模型的预测准确性与预测趋势变化的能力均有所提高.分析表明,利用MM模型对不同类型时空过程建模时,不同分量信息所起到的作用并不相同.     

ANALYSIS OF PSYCHOLOGIC HEALTH STATE AND INFLUENCING FACTORS IN COLLEGE AND SECONDARY SCHOOL STUDENTS IN SHAANXI PROVINCE

It is necessary for educationists to knowstudents psychologic health state in colleges andmiddle schools.In recent years,there are lots of in-vestigations and researches on students psycholog-ic health state in colleges and middle schools.Re-sults have shown thatthe percentage ofcollege andmiddle school students psychologic disorder is be-tween1 0 and30 [1～ 5] .Shaanxi province lies in theNorthwestof China,thus its economics is compara-tively less developed.And also,there are lots ofcolleges…