车路协同环境下自适应信号配时优化模型

在车路协同环境下,车辆位置和速度等信息容易获得,为交通信号控制系统提供了新的数据源。针对现有信号控制系统鲁棒性差,不能适应交通流实时变化特征等问题,本文提出了一种车路协同环境下交叉口自适应实时控制优化模型。该模型以交叉口车均延误最小为优化目标,相位绿灯时长为约束条件,采用遗传算法对模型进行求解,实现了对交叉口信号配时方案的实时优化。最后,通过调查数据并设计仿真实验,证明了文中模型比感应控制效果更好,车辆平均延误减少了30%,同时能够保证交叉口各个转向的车均延误均衡。     

基于自适应遗传算法的双环信号配时优化模型

为了有效利用交叉口时空资源,缓解城市交通拥堵,在双环相位方案的基础上,建立以交叉口平均延误为优化目标,以各相位绿灯时长为优化参数的双环信号配时优化模型,并采用自适应遗传算法对模型进行求解。选取4个十字交叉口,实地调查获得交叉口晚高峰流量数据,分别使用经典的Webster配时法和双环信号配时优化模型对交叉口信号配时方案进行优化。结果表明：与Webster配时法相比,双环信号配时优化模型优化结果更好,分别使4个交叉口平均延误减少11.36%、13.74%、3.72%和9.00%,能够有效改善交叉口运行状况;并且交叉口同相位内的两个流向的流量越不均衡,双环信号配时优化模型优化结果越好。     

改进的遗传算法及其在交通信号优化中的应用

以典型的四相位交叉口为例,基于改进的遗传算法,研究了在相对固定周期条件下集合交通流量变化而实时调整配时方案的优化算法,利用各进道口上车辆平均延误时间最小为目标建立目标优化函数,通过仿真分析对比,证明改进以后的遗传算法用于交通配时优化问题是有效的。     

用神经网络和遗传算法优化电镀锌镍磷工艺参数

提出了一种神经网络与遗传算法相结合的电镀锌镍磷合金工艺参数优化方法.以试验数据为样本,通过神经网络建立电镀工艺参数与电镀性能关系之间的复杂模型,利用遗传算法对电镀工艺参数进行优化,可充分发挥神经网络的非线性映射能力和遗传算法的全局寻优能力.试验显示了方法的有效性和优越性.     

网联交通环境交叉口公交优先信号滚动优化模型

该文提出一种基于网联车辆实时数据的单交叉口公交优先信号配时滚动优化方法。建立了以所有出行者的总出行时间最小化为目标的混合整数线性规划(MILP)模型,采用灵活的信号框架,不限制信号周期和固定的相位组合及相序,并进一步提出了滚动优化执行框架。信号配时优化方法不要求交叉口设置公交专用道,而且可在优先考虑公交的同时,避免对普通车辆的通行产生的不公平的负面影响。仿真结果表明,与触发响应式的传统公交优先方法相比,该文中公交优先信号配时优化模型可以显著减少车均及人均延误,同时可使交叉口通行能力提高25%以上。     

汽车车内噪声主动控制系统扬声器与麦克风布放优化方法

利用声固耦合边界元仿真方法与多目标遗传算法,实现了面向对象的车内噪声主动控制(ANC)系统扬声器麦克风布放方案的优化。首先基于自适应算法,推导了车内噪声主动控制系统降噪性能预测方法,并利用声固耦合边界元仿真方法,实现了面向对象的ANC系统降噪性能预测;在该仿真模型的基础上,建立对应的代理模型,以实现对系统降噪性能的快速预测;最后利用多目标遗传算法,获得系统关于扬声器麦克风数量与多个频率下降噪量的Pareto最优解集。该最优解集能定量描述ANC系统扬声器麦克风数量与降噪性能之间的关系,并为该系统与车辆的匹配提供依据。     

交叉口间接左转多目标信号协调配时优化

为提升城市交叉口主干交通的通行效率，探究在“右转-掉头-直行”间接左转模式下对车流进行信号协调控制的配时方法。在模型构建部分，兼顾主干车流、转向车流和行人通行效益，使用模糊偏好法分配权重，构建多目标优化模型，并采用粒子群算法求解。在实例分析部分，选取典型交叉口为分析对象，以四相位等饱和度的信号配时设计作为常规方案，以间接左转的信号协调控制作为改进方案，对比两者的通行效益。结果表明，改进方案下主干车流延误降低50.8%，通行能力提升57.6%，左转车流延误升高31.2%，交叉口整体延误降低26.3%，主干交通通行状况改善明显。     

基于遗传算法优化BP神经网络的可食用油墨粘度的预测

目的 通过研究遗传算法优化BP神经网络建立自变量与因变量之间的关系,从而对可食用油墨的粘度进行预测和模拟.方法 在前期关于可食用油墨的研究基础上,以醋酸浓度、壳聚糖用量、酒精用量、研磨速度为自变量,以配制得到的油墨粘度作为因变量,利用正交实验设计实验,运用BP神经网络结合遗传算法对可食用油墨的粘度进行预测和模拟.结果 以正交实验设计得到30组实验数据,利用Matlab 2018a软件中GAOT遗传算法工具箱,经过38次迭代训练,得到收敛精度为10-4的神经网络,粘度的预测值与对应的真实值相对误差介于0.05％～3.7％,拟合度R2值为0.8672,表明该神经网络对可食用油墨的粘度具有较好的预测能力和较高的预测精度.结论 遗传算法优化BP神经网络可以用来预测和模拟可食用油墨的粘度,可以将神经网络拓展到可食用油墨其他性能的评价体系中,从而对可食用油墨的生产和应用提供指导性的建议.     

城市交通信号灯的自动控制设计分析

在分析城市交通信号控制研究现状的基础上,提出一种基于神经网络模糊控制的单路口交通信号灯控制方法,通过检测当前相位的排队长度和下一相位的排队长度得出当前相位以及下一相位的车流密度,进而判断是否进行相位变换,以每个周期内交叉口的车辆平均延误作为控制指标,来判断该控制器的控制性能。本文根据城市交通系统的实际状况,依据交叉口个方向车流的来车信息,对等待车队长度、延长时间等进行模糊语言描述,从而对车流动态信息采用模糊控制规则与运算规则进行描述,得出相应的调度模式。设计了模糊控制器,并对该控制器进行了仿真研究,结果表明该方法应用方便可靠,可以有效地改善交叉路口的通行能力,为优化城市交通控制提供了一种参考方法。     

基于整车分析的磁流变减振器优化研究EI北大核心CSCD

针对现有车用磁流变减振器设计优化过程中的不足,全面分析了设计参数约束关系并筛选出核心设计参数。针对磁场中的重要设计参数设计动态约束条件,并基于磁场有限元仿真结果采用神经网络来拟合参数边界模型,缩小优化范围;基于天棚阻尼控制思想设计了多参数反馈整车控制算法并进行了参数优化。创建了包含整车模型、控制器模型、减振器模型、执行器响应模型的联合仿真平台,以车身总加权加速度为指标,使用遗传算法对减振器设计参数进行了全局优化,结果表明优化后的减振器能更好的满足悬架控制需求,配合整车控制算法可以有效改善车辆行驶平顺性。     

Connected Vehicle-Based Traffic Signal Coordination

This study presents a connected vehicles (CVs)-based traffic signal optimization framework for a coordinated arterial corridor. The signal optimization and coordination problem are first formulated in a centralized scheme as a mixed-integer nonlinear program (MINLP). The optimal phase durations and offsets are solved together by minimizing fuel consumption and travel time considering an individual vehicle’s trajectories. Due to the complexity of the model, we decompose the problem into two levels: an intersection level to optimize phase durations using dynamic programming (DP), and a corridor level to optimize the offsets of all intersections. In order to solve the two-level model, a prediction-based solution technique is developed. The proposed models are tested using traffic simulation under various scenarios. Compared with the traditional actuated signal timing and coordination plan, the signal timing plans generated by solving the MINLP and the two-level model can reasonably improve the signal control performance. When considering varies vehicle types under high demand levels, the proposed two-level model reduced the total system cost by 3.8% comparing to baseline actuated plan. MINLP reduced the system cost by 5.9%. It also suggested that coordination scheme was beneficial to corridors with relatively high demand levels. For intersections with major and minor street, coordination conducted for major street had little impacts on the vehicles at the minor street.     

Aging process optimization for a copper alloy considering hardness and electrical conductivity

A multi-objective optimization methodology for the aging process parameters is proposed which simultaneously considers the mechanical performance and the electrical conductivity. An optimal model of the aging processes for Cu–Cr–Zr–Mg is constructed using artificial neural networks and genetic algorithms. A supervised artificial neural network (ANN) to model the non-linear relationship between parameters of aging treatment and hardness and conductivity properties is considered for a Cu–Cr–Zr–Mg lead frame alloy. Based on the successfully trained ANN model, a genetic algorithm is adopted as the optimization scheme to optimize the input parameters. The result indicates that an artificial neural network combined with a genetic algorithm is effective for the multi-objective optimization of the aging process parameters.     

基于神经网络遗传算法的GFRP拉挤双目标优化

为研究玻璃钢(GFRP)拉挤工艺参数对复合材料性能的影响,优化最佳拉挤工艺参数,建立了拉挤工艺过程数学模型,结合基于有限元/有限差分的间接解耦法进行求解,模拟得到了拉挤过程中GFRP内部的非稳态温度场和固化度变化情况.分别采用布拉格光栅光纤温度传感器和索氏萃取法检测拉挤GFRP内部的温度与固化度,实测温度和固化度均与模拟温度和固化度吻合,验证了数值模拟程序的正确性.以数值模拟结果为样本,建立反向传播神经网络,得到拉挤工艺参数(固化温度、拉挤速度)与GFRP固化度之间的非线性相关关系,再结合遗传算法解决拉挤过程中固化炉温度和拉挤速度双目标优化问题.优化得到的拉挤工艺参数可在保证复合材料固化度达标的情况下,提高拉挤速度降低固化炉温度,优化效果显著.神经网络遗传算法优化方法能有效解决此类具有复杂非线性关系的多目标优化问题.     

基于多目标遗传算法的实验目标车底盘结构优化

在实际道路上开展实验目标车碰撞试验是智能汽车开发过程中的重要测试手段之一.为保证智能汽车的安全性,实验目标车的底盘必须具有抵抗较大碾压冲击的能力.由于实验目标车底盘的碾压过程涉及接触非线性和几何非线性等问题,仅基于仿真分析对其进行结构设计和优化存在较大困难.为此,首先采用显式非线性有限元法对实验目标车底盘的抗碾压性能进...     

Neural networks to model dynamic systems with time delays

An algorithm is proposed to identify a neural network model that represents a nonlinear dynamic system with a multivariate time delay response. The algorithm consists of two major parts. The first one identifies the time delay vector for a given neural network structure. This task is accomplished by using an exhaustive integer enumeration algorithm that minimizes a statistical parameter to assess the performance of the neural network model. The second part uses a cross-validation strategy to identify the best neural network model. Since the structure that models a nonlinear system is usually unknown, the identification strategy consists of selecting several neural network structures and identifying the best time delay vector for each network. The modeling process starts with the simplest structure and progressively the complexity of the network is increased to end up with a complex structure. Finally, the network that offers the simplest structure with the best network performance is the one that exhibits the appropriate neural network structure with the corresponding optimal time delay vector. The Monte Carlo simulation technique was used to test the performance of the algorithm under the presence of linear and nonlinear relationships among several variables of dynamic systems and with a different time delay applied to each input variable. The introduced algorithm is used to detect a chemical reaction delay among enriched amyl acetate, acetic acid, water, and the pH of erythromycin sail. An appropriate neural network model was designed to model the pH of the erythromycin during a continuous extraction process. To the best of the authors knowledge the proposed algorithm is the only one currently available to identify time delay interactions in the multivariate input output variables of a system. The major drawback of the introduced algorithm is that it becomes very slow as the number of system inputs increases. This algorithm works efficiently in a system that involves five inputs or less.     

基于遗传神经网络的异步电动机故障诊断研究

提出一种基于遗传神经网络进行异步电机故障检测的新方法,仅利用一个振动传感器来获取异步电机的特征信息,建立电机动态非线性神经网络检测诊断模型,并利用该模型进行电机的故障检测,为减少网络权值学习搜索空间,解决神经网络权值学习中易于陷入局部最小点的问题,本文采用遗传算法实现模型权值的修正,实际使用证明利用该方法可以方便的实现在线故障诊断,且方法简单,易于实现。     

基于粒子群优化算法和BP神经网络的变频压缩机功率预测

本文针对变频压缩机的功率测量困难,测量误差大等问题,提出了一种仿真测量模型。利用粒子群算法寻找全局最优粒子,用它初始化BP神经网络的阈值和权值,测量变频压缩机的功率。本文共建立了3种仿真模进行对比,分别为BP神经网络模型、GA-BP神经网络模型和PSO-BP神经网络模型,然后分别通过3种模型的内插、蒸发温度外推和冷凝温度外推的测试方法对变频压缩机进行功率测量,对比分析其预测结果的平均相对误差和拟合程度。结果表明:基于粒子群算法优化的BP神经网络模型明显优于其他两个模型,特别是在冷凝温度外推测试中,较其他两个神经网络相对误差降低了1. 11%、2. 64%,3种测试方法下的平均相对误差均小于1%,拟合程度在0. 9以上,表明基于粒子群算法优化的BP神经网络模型对变频压缩机功率有较好的测量能力,而且有较强的泛化能力。     

Timing and frequency offset estimation scheme for the uplink of OFDMA systems

Timing and frequency synchronisation for the uplink of OFDMA systems is discussed. The uplink synchronisation procedure is presented and a novel timing and frequency offset estimation scheme is proposed. The timing and frequency offsets are estimated by identifying the differential phases of the training subcarriers in frequency and time dimensions, respectively. The frequency offset is estimated ahead of the timing offset, after which intercarrier interference compensation is carried out based on the estimated frequency offset. Finally, the timing offset is estimated after eliminating the frequency offset's influence. The principle of best linear estimation is applied. Both the case of a single user and that of multiple users simultaneously accessing the network are considered. In contrast to other methods, the proposed scheme has moderate complexity and allows flexible subcarrier assignment schemes. The analyses and simulation results demonstrate the accuracy of the proposed scheme in the uplink channels.     

A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters

This paper presents a hybrid optimization method for optimizing the process parameters during plastic injection molding (PIM). This proposed method combines a back propagation (BP) neural network method with an intelligence global optimization algorithm, i.e. genetic algorithm (GA). A multi-objective optimization model is established to optimize the process parameters during PIM on the basis of the finite element simulation software Moldflow, Orthogonal experiment method, BP neural network as well as Genetic algorithm. Optimization goals and design variables (process parameters during PIM) are specified by the requirement of manufacture. A BP artificial neural network model is developed to obtain the mathematical relationship between the optimization goals and process parameters. Genetic algorithm is applied to optimize the process parameters that would result in optimal solution of the optimization goals. A case study of a plastic article is presented. Warpage as well as clamp force during PIM are investigated as the optimization objectives. Mold temperature, melt temperature, packing pressure, packing time and cooling time are considered to be the design variables. The case study demonstrates that the proposed optimization method can adjust the process parameters accurately and effectively to satisfy the demand of real manufacture.     

基于有限元的三辊行星轧制力预测及分析

为预测三辊行星轧制力,建立了三维热力耦合有限元模型,用均匀设计法安排有限元模拟样本,采用基于LM算法的BP神经网络学习有限元轧制力结果,确定了轧制参数与轧制力的映射关系,并根据训练后的神经网络分析了摩擦系数、轧辊偏转角和轧辊转速对轧制力影响．预测结果表明：摩擦系数和轧辊偏转角对轧制力影响是多方面的,在高轧辊转速时,较大的摩擦系数有利于降低轧制力．