基于神经网络的电子地图最优路径研究

现阶段电子地图的应用已经深入到各行各业,最优路径的查找及规划作为GIS的一个关键问题,成为研究的一个难点,本文提出一种基于Hopfield神经网络的方法来解决地图最优路径问题。以物流配送中的车辆路径最优化为例,先根据Hopfield神经网络模型研究基于Hopfield的最优路径算法,然后探讨和验证该算法的可行性、实用性,最后通过算例分析对该方法进行验证。     

考虑蓄水期弃水风险的水库长期发电调度方法

短期径流的大幅波动使得以平均径流为基础制定的水库长期调度方案面临较大弃水风险,是影响水库长期调度决策合理性的重要因素。本文考虑日尺度径流波动影响,提出一种蓄水期弃水风险量化方法,并建立了耦合弃水风险的水库长期发电调度模型。利用长系列日径流资料,结合风险最小蓄水规则,以不蓄弃水流量为指标量化蓄水期各月弃水风险;采用Copula函数构建月均入流与弃水风险的联合分布和条件概率分布,明晰了特定入流条件的风险置信区间;最后,将弃水风险以弃电损失函数融入优化模型,以获得更符合实际的长期调度方案。以澜沧江流域小湾水库为工程背景进行调度模拟分析,结果表明本文方法能够有效降低水库长期优化调度方案的弃水风险,有利于提高优化结果的可操作性,与传统方法相比,能够使多年平均弃水减少约4.76亿m³、发电量增加约1.15亿kW·h。     

金沙江中游梯级水电站联合优化调度实践

分析了金沙江中游区域梯级水电站联合调度的特殊性及复杂性,从电调的角度介绍了金中集控中心自成立以来对受控梯级水电站联合调度的具体实施,总结了梯级水电站联合优化调度的措施,并分析了梯级水电站联合优化调度的综合效益,发现优化调度成效明显。     

安全高效矿井监控关键技术研究

提出安全高效矿井应采用先进可靠的监控技术,实现供电、排水、通风、压风、运输、提升、瓦斯抽采等固定岗位无人值守和地面远程控制,综采、综放等采煤工作面少人作业和地面远程控制;提出供电、排水、通风、压风、运输、提升、瓦斯抽采等监控系统应具有地面远程控制功能;提出具有远程控制、报警联动、调度指挥等功能的煤矿调度指挥控制中心;提出整合计算机网络机房、程控交换机机房、调度交换机机房、监控系统机房等的数据中心;提出基于煤层瓦斯压力和瓦斯含量、综合指标、钻屑瓦斯解吸指标、复合指标、R值指标、瓦斯涌出量(根据瓦斯体积分数和风量计算)、巷道位置、微震、地音、温度等及其变化的煤与瓦斯突出预警方法;提出基于WiFi的矿用无线传输接口,以便于互通互联;提出通过分布式光纤测温预警煤炭自然发火,较通过监测CO等煤炭自然发火标志气体更直接、更及时、更可靠;提出根据运煤量实时调整输送带速度:当煤量较小时降低输送带运行速度,当煤量较大时提高输送带运行速度,以提高运输效率,减少设备磨损;提出基于光纤综合保护的供电监控系统具有防越级跳闸、地面远程整定和地面远程控制功能,提高了煤矿供电的可靠性,可实现煤矿井下机电硐室无人值守;提出具有煤岩识别与滚筒自动调高,采煤机、刮板机、液压支架3机联动,记忆割煤,地面远程、顺槽近程、手动控制和紧急停机,放顶煤量和煤矸控制,采煤机和刮板机等大型机电设备故障诊断等功能的采煤工作面监控系统。     

基于星敏感器的姿态优化估计算法

对于采用星敏感器的航天器姿态确定问题,提出了一种快速的姿态优化估计方法;首先,根据Rodrigues参数和观测向量之间的线性关系,引入四元数,构造了不同的非奇异的优化准则;其次,借助四元数,求解了基于该准则的姿态优化算法;误差协方差分析和数值仿真结果表明,该算法与著名的QUaternion ESTmation(QUEST)算法相比,在相同的操作系统环境下,具有相同的姿态估计精度,但计算速度更快,对两种算法选取间隔为2000次的仿真步长,统计数据长度为100个采样点,最后对结果加权求平均值,统计结果显示仅为QUEST的三分之一;这种姿态优化估计方法具有一定的工程应用价值。     

多时滞线性连续系统的最优H∞容错控制

针对状态具有多个时滞的线性连续系统,基于Lyapunov稳定性理论和线性矩阵不等式方法(LMI),采用无时滞记忆的状态反馈控制律,研究了在执行器发生故障的情况下,连续多时滞系统的最优H∞容错控制问题.首先,给出了系统没有干扰输入时存在无记忆状态反馈容错控制器的一个充分条件;进一步,给出了在H∞扰动衰减指标约束下,系统存在无记忆状态反馈H∞容错控制器的一个充分条件;最后,给出了最优无时滞记忆状态反馈H∞容错控制器的设计方法.仿真实例证明了所得最优H∞容错控制嚣设计方法的正确性和有效性.     

Optimal tracking performance of MIMO discrete‐time systems with communication constraints

The optimal tracking problem for multiple‐input multiple‐output linear‐time‐invariant discrete‐time systems with communication constraints in the feedback path is studied in this paper. The tracking performance is measured by the energy of the error signal between the output of the plant and the reference signal. The objective is to obtain an optimal tracking performance, attainable by all possible stabilizing compensators. It is shown that the optimal tracking performance consists of two parts, one depends on the nonminimum phase zeros and zero direction of the given plant, as well as the reference input signal direction, and the other depends on the nonminimum phase zeros, unstable poles, and pole direction of the given plant, as well as the bandwidth and additive white Gaussian noise of the communication channel. It is also shown that, if the constraint of the communication channel does not exist, the optimal tracking performance reduces to the existing tracking performance of the control system without communication constraints. A typical example is given to illustrate the theoretical results. Copyright © 2011 John Wiley & Sons, Ltd.     

Handling multiple objectives with biogeography-based optimization

Biogeography-based optimization (BBO) is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization (BBMO) is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability effciently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II (NSGA-II) and archive-based micro genetic algorithm (AMGA) in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do.     

Optimal Performance in Tracking Stochastic Signal Under Disturbance Rejection

The analysis method of optimal tracking performance is proposed for multiple‐input multiple‐output (MIMO) linear time‐invariant (LTI) systems under disturbance rejection. An H₂ criterion of the error signal between the output of the plant and the reference signal is used as a measure for the tracking performance. Spectral factorization is applied to obtain the optimal solution of the system tracking error. The explicit expressions are derived for this minimal tracking error with respect to random reference signals under disturbance rejection. It is shown that the nonminimum phase zeros, the zero direction, the unstable poles, the pole direction of a given plant, statistical characteristics of the reference input signal, and disturbance signal have a negative effect on a feedback system's ability to reduce the system error with disturbance rejection. The results show that the optimal tracking performance will further be damaged because of disturbance rejection. Some typical examples are given to illustrate the theoretical results.