粒子群算法在电梯群控调度中的应用

随着电梯的广泛使用,人们对电梯的研究也越来越多,针对乘客乘候梯时间过长、电梯能耗性能不理想的问题提出了一种基于粒子群算法的电梯群控调度方法。首先选取乘客乘梯时间、候梯时间、电梯电能损耗量建立电梯群控系统的多目标优化模型,使用粒子群算法对模型进行优化;然后设计仿真实验用乘客的平均乘梯时间、候梯时间以及电梯的起停次数这几个指标与遗传算法进行对比,最后的实验结果说明将粒子群优化算法应用到电梯群控调度中能够具有更好的表现。     

基于PLC和触摸屏的电梯群控系统设计

针对具体的二座四层群控电梯,描述了基于PLC和触摸屏的电梯群控的实现过程。针对单梯控制,主要叙述了电梯平层的处理,触摸屏控制和显示的结构和工程设计。在此基础上,给出了基于触摸屏、PLC、变频器的电梯群控的系统结构,叙述了群控电梯PLC组之间的通信设定、群控电梯平层的处理、电梯群控调度算法,并进行了不断优化。     

基于粒子群优化的公交调度研究

针对公交调度优化问题,建立了以公交费用最小和乘客平均等待时间最短为目标的优化调度模型。应用改进的粒子群优化算法对公交调度排班进行优化,并用实际的运营数据对算法进行验证。结果表明,该求解算法能够兼顾了公交公司和乘客的利益,是可行和有效的。     

电梯群控预约控制算法

根据电梯群控系统特性，借鉴有关文献，提出了一种多目标控制算法：电梯群控预约优化算法，斤层增设目的层预约按钮，计算机能够从厅层监督系统和入检测子系统获取及时候梯人数、轿内拥挤度等信息，然后采集未的数据计算每个电梯的评价函数，最后选择适合的电梯响应斤层召唤。     

基于粒子群优化的传感器管理算法研究

本文在分析基于二进制粒子群优化的传感器管理算法缺点的基础上,通过对粒子的降维处理和位置矢量更新式的改进,提出了一种基于实值粒子群优化的传感器管理算法.并针对中段弹道目标跟踪这一特殊应用背景,分析跟踪的约束条件,提出了一种新的优化目标函数.通过对中段弹道目标跟踪典型场景下的仿真实验分析,给出了目标函数加权系数的优选方案,并对所提方法的性能和适用范围进行了详细分析和比较.仿真实验表明,基于实值粒子群优化的传感器管理算法是一种更加高效的方法.     

一种新的基于值函数迁移的快速Sarsa算法

知识迁移是当前机器学习领域的一个新的研究热点.其基本思想是通过将经验知识从历史任务到目标任务的迁移,达到提高算法收敛速度和收敛精度的目的.针对当前强化学习领域中经典算法收敛速度慢的问题,提出在学习过程中通过迁移值函数信息,减少算法收敛所需要的样本数量,加快算法的收敛速度.基于强化学习中经典的在策略Sarsa算法的学习框架,结合值函数迁移方法,优化算法初始值函数的设置,提出一种新的基于值函数迁移的快速Sarsa算法--VFT-Sarsa.该算法在执行前期,通过引入自模拟度量方法,在状态空间以及动作空间一致的情况下,对目标任务中的状态与历史任务中的状态之间的距离进行度量,对其中相似并满足一定条件的状态进行值函数迁移,而后再通过学习算法进行学习.将VTF-Sarsa算法用于Random Walk问题,并与经典的Sarsa算法、Q学习算法以及具有较好收敛速度的QV算法进行比较,实验结果表明,该算法在保证收敛精度的基础上,具有更快的收敛速度.     

基于粒子群算法的嵌入式云计算资源调度

随着移动互联网的发展,基于嵌入式设备的云计算服务成为研究热点。在国内,嵌入式云计算目前正处于探索研究阶段,云资源管理调度是嵌入式云计算的核心技术之一,其效率直接影响嵌入式云计算系统的性能。为了提高云计算性能,本文提出一种基于粒子群优化算法的云计算任务调度模型。粒子群算法中粒子位置代表可行的资源调度方案,以云计算任务完成时间及资源负载均衡度作为目标函数,通过粒子群优化算法,找出最优资源调度方案。在matlab实验平台进行了仿真,通过大量数据模拟实验表明,该模型可以快速找到最优调度方案,提高资源利用率,具有较好的实用性和可行性。     

分组扰动的粒子群优化算法在车辆调度中的应用

车辆调度是一个复杂的系统,具有多目标控制、高度非线性、时变等特征。基于粒子群算法,本文引入了分组扰动的思想,将其应用于高铁施工现场混凝土预拌车调度方案优化问题中。通过对一个实例的仿真研究表明,该算法可以极大限度地同时满足混凝土拌合站和工地的利益,能够很好地解决车辆调度优化问题。     

基于深度强化学习的医用设备应急调度优化技术研究

针对大型医用设备人工管理效率低、无法满足应急调度需求的问题,文中提出了基于深度强化学习算法的医用设备应急调度优化技术.使用物联网技术采集大型医用设备日常使用的各类参数,作为后续调度优化算法的样本数据.通过对医用设备调度问题的分析,采用马尔可夫决策过程作为调度优化算法的基础模型,并给出了状态空间、动作空间以及奖惩函数的定...     

改进粒子群算法的目标函数变化分类动态优化

由于优化问题的目标函数和约束条件都随着时间而改变导致其最优值也发生改变,提出一种基于改进粒子群算法的目标函数变化分类动态优化算法。首先对动态优化问题进行定义,明确问题的研究对象,提出对目标函数随时间变化程度分类的思想,通过对变化的函数进行监测的方法将其分为剧烈变化、中等程度变化和弱变化三种类型,并针对不同的强度变化对粒子群算法采用不同的改进策略,最后将不同的策略融入计算。通过采用移动多峰问题进行测试,结果表明,提出的改进粒子群优化算法能监测目标函数变化,并能随时跟踪到最优解,平均离线误差相对于标准粒子群算法更小,性能更稳定。     

Adaptive learning algorithm for Cerebellar model articulation controller

Cerebellar model articulation controller (CMAC) was developed two decades ago, yet lacks an adequate learning algorithm. Examining the performance of a CMAC based controller showed that the control system become unstable after a long period of real time runs. A new adaptive learning algorithm is proposed. The resultant controller is applied for the trajectory tracking control of a piezoelectric actuated tool post. The performance of the proposed controller is compared with those of conventional controllers (PI controller and the conventional CMAC based controller). The experimental results showed that performance of the CMAC based controller using the proposed learning algorithm is stable and more effective than that of the conventional controllers.     

Implementation of LLCC-resonant driving circuit and adaptive CMAC neural network control for linear piezoelectric ceramic motor

In this paper, an adaptive cerebellar-model articulation computer (CMAC) neural network (NN) control system is developed for a linear piezoelectric ceramic motor (LPCM) that is driven by an LLCC-resonant inverter. The motor structure and LLCC-resonant driving circuit of an LPCM are introduced initially. The LLCC-resonant driving circuit is designed to operate at an optimal switching frequency such that the output voltage will not be influenced by the variation of quality factor. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive CMAC NN control system is designed without mathematical dynamic model to control the position of the moving table of the LPCM drive system to achieve high-precision position control with robustness. In the proposed control scheme, the dynamic backpropagation algorithm is adopted to train the CMAC NN online. Moreover, to guarantee the convergence of output tracking error for periodic commands tracking, analytical methods based on a discrete-type Lyapunov function are utilized to determine the optimal learning-rate parameters of the CMAC NN. The effectiveness of the proposed driving circuit and control system is verified by experimental results in the presence of uncertainties, and the advantages of the proposed control system are indicated in comparison with a traditional integral-proportional position control system. Accurate tracking response and superior dynamic performance can be obtained due to the powerful online learning capability of the CMAC NN with optimal learning-rate parameters.     

Towards a Sustainable Green Design for Next-Generation Networks

Recently distributed real-time database systems are intended to manage large volumes of dispersed data. To develop distributed real-time data processing, a reality and stay competitive well defined protocols and algorithms must be required to access and manipulate the data. An admission control policy is a major task to access real-time data which has become a challenging task due to random arrival of user requests and transaction timing constraints. This paper proposes an optimal admission control policy based on deep reinforcement algorithm and memetic algorithm which can efficiently handle the load balancing problem without affecting the Quality of Service (QoS) parameters. A Markov decision process (MDP) is formulated for admission control problem, which provides an optimized solution for dynamic resource sharing. The possible solutions for MDP problem are obtained by using reinforcement learning and linear programming with an average reward. The deep reinforcement learning algorithm reformulates the arrived requests from different users and admits only the needed request, which improves the number of sessions of the system. Then we frame the load balancing problem as a dynamic and stochastic assignment problem and obtain optimal control policies using memetic algorithm. Therefore proposed admission control problem is changed to memetic logic in such a way that session corresponds to individual elements of the initial chromosome. The performance of proposed optimal admission control policy is compared with other approaches through simulation and it depicts that the proposed system outperforms the other techniques in terms of throughput, execution time and miss ratio which leads to better QoS.

     

Optimal design of CMAC neural-network controller for robotmanipulators

This paper is concerned with the application of quadratic optimization for motion control to feedback control of robotic systems using cerebellar model arithmetic computer (CMAC) neural networks. Explicit solutions to the Hamilton-Jacobi-Bellman (H-J-B) equation for optimal control of robotic systems are found by solving an algebraic Riccati equation. It is shown how the CMAC can cope with nonlinearities through optimization with no preliminary off-line learning phase required. The adaptive-learning algorithm is derived from Lyapunov stability analysis, so that both system-tracking stability and error convergence can be guaranteed in the closed-loop system. The filtered-tracking error or critic gain and the Lyapunov function for the nonlinear analysis are derived from the user input in terms of a specified quadratic-performance index. Simulation results from a two-link robot manipulator show the satisfactory performance of the proposed control schemes even in the presence of large modeling uncertainties and external disturbances     

CMAC与大林的复合控制在纯滞后系统中的应用

一般工业控制中都会存在纯滞后现象,针对于纯滞后性质对控制系统稳定性的影响,采用在大林算法的基础上加入CMAC(小脑神经网络)的方法,CMAC用于前馈控制,对大林控制器的输出进行学习,从而提高系统的响应速度,克服大林算法调节时间长的缺点。文中给出了在MATLAB中编写的M文本文件控制器的仿真结果,进而将控制算法编写成函数文件,运用到SIMULINK仿真中,大大的提高了控制算法的实用性。     

强化学习算法在雷达智能抗干扰中的应用

雷达在工作过程中所应对的干扰场景复杂且多变,所具有的反干扰措施难以穷举。人工设计的反干扰流程与抑制策略在面对这些对抗场景时,由于受限于专家的经验知识,其反干扰性能难以保证。对此,文中从雷达抗干扰的应用需求出发,通过引入强化学习方法,提出一种基于强化学习模型的智能抗干扰方法。分别利用Q学习与Sarsa两种典型的强化学习算法对反干扰模型中的值函数进行了计算并迭代,使得反干扰策略具备了自主更新与优化功能。仿真结果表明,强化学习算法在训练过程中能够收敛并实现反干扰策略的优化。相比于传统的反干扰设计手段,雷达反干扰的智能化程度得到了有效提升。     

基于改进强化学习的PID参数整定原理及应用

控制系统的响应特性取决于控制律参数,经典的PID方法难以实现参数的自整定。强化学习能够通过系统自身和环境的交互实现参数的自动调整,但是在控制律参数需要频繁调整的应用场合,常规的强化学习方法无法满足实时性要求,而且容易陷入局部收敛。对传统的强化学习方法加以改进后,加快了在线学习速度,提高了强化学习算法的寻优能力。仿真结果表明,该方法可以在一定范围内快速求得全局最优解,提高控制系统的自适应性,为控制系统参数的自整定提供了依据。     

基于单片机控制的模拟电梯设计与实现

搭建了一套模拟电梯的硬件平台,设计了VB界面模拟电梯按键,采用查找算法作为电梯调度算法,通过设计和实现该算法来验证平台的有效性。该平台可移植其他算法,用来验证电梯控制系统功能。系统采用STC89C52作为电梯控制器,实现了电梯手动和自动的开、关门功能,当前电梯楼层显示功能,开门铃声提醒功能,电梯当前状态显示功能,关门倒计时功能,根据时间优先原则、顺向优先原则和最远反向截梯控制原则对用户请求作出先后响应的功能。     

Optimal admission control in multimedia mobile networks with handover prediction [dependability in the ubiquitous wireless access]

Admission control is one of the key traffic management mechanisms that must be deployed in order to meet the strict requirements for dependability imposed on the services provided by modern wireless networks. We study the problem of optimizing admission control policies in mobile multimedia cellular networks when predictive information regarding the movement of mobile terminals is available. For the optimization process we deploy a novel reinforcement learning approach based on the concept of afterstates. The results obtained define theoretical limits for the gain that can be expected when using handover prediction, which cannot be established by deploying heuristic approaches. Numerical results show that the performance gain is a function of the anticipation time with which the admission controller knows the occurrence of handovers, and an optimal anticipation time exists. We also compare an optimal policy obtained deploying our approach with a previously proposed heuristic prediction scheme, showing that there is still room for technological innovation.     

基于强化学习的交叉口交通低排放信号控制研究

交叉口车辆排放较为复杂,尤其是在考虑初始排队长度的情况下,更是难以建立明确的数学模型。Q学习是一种无模型的强化学习算法,通过与环境的试错交互学习最优控制策略。本文提出了一种基于Q学习的交通排放信号控制方案。利用仿真平台USTCMTS2.0,通过不断地试错学习找到在不同相位排队长度下最优配时。在Q学习中添加了模糊初始化Q函数的方法以改进Q学习的收敛速度,加速了学习过程。仿真实验结果表明:强化学习算法取得较好的效果。相比较Hideki的方法,在车流量较高时,车辆平均排放量减少了13.9%,并且对Q函数值的模糊初始化大大加速了Q函数收敛的过程。