利用卡尔曼滤波的粒子群空间映射优化

文章提出了一种粒子群空间映射优化方法,将空间映射思想与粒子群优化相结合。与其他仅找到满足设计要求的解决方案的空间映射方法不同,粒子群空间映射优化方法可以找到最佳解决方案。此外,在粒子群空间映射优化中引入卡尔曼滤波理论,建立了粗糙模型与精细模型之间的映射关系。卡尔曼滤波映射关系对粗糙模型计算的代价影响很小,优化实例表明,提出的粒子群空间映射优化具有良好的性能,可以显著降低优化的成本。

基于双通道生成对抗网络的城市用电负荷缺失数据补全方法

用电负荷数据的完整性与有效性在负荷预测等应用中具有重要意义。传统的缺失数据补全方法缺乏对用电负荷和多种外部时空关联信息的挖掘,难以获得高精度的补全结果。文中提出了一种双通道生成对抗网络,对缺失的负荷数据进行补全。首先,根据负荷的周期性变化特征和时空关联性构建三阶负荷张量,并将影响负荷变化的多种外部因素构建为三阶辅助信息张量。然后,为满足两种张量的双输入需求,在生成对抗网络的输入层引入双通道机制,通过卷积与反卷积运算提取张量的特征;为提升网络对张量数据的训练效果和补全精度,将张量分解损失引入原始损失函数,并采用改进的混沌映射粒子群优化算法联合优化超参数和网络。最后,在真实负荷数据集上开展数据补全实验。结果表明,所提方法能够对随机缺失率不超过50%、连续缺失不超过3天的负荷数据进行准确补全。

基于改进粒子群算法的水轮机调节系统分数阶PIλDμ控制器设计

为提高水轮机调节系统动态调节品质,建立了计入弹性水击效应的水轮机调节系统非线性模型,并据此提出一种基于改进粒子群算法的分数阶PIλDμ(fractional-order PID,FOPID)控制器设计方法。该方法以基于最优参数(b=5.387 8,d=0.752 6)的改进Oustaloup滤波算法构建FOPID控制器,并利用融合有参数自适应调整策略及基于改进Tent映射的变尺度混沌局部搜索的改进粒子群算法(improved adaptive chaos PSO,IACPSO)对FOPID控制器参数进行优化选择。仿真结果表明,与Oustaloup算法及基于推荐参数(b=10,d=9)的改进Oustaloup算法相比,基于最优参数的改进Oustaloup算法对任意阶次的分数阶微分算子均具有更高的逼近精度,更宽的理想拟合频域段;提出的IACPSO算法求解精度高、稳定性好、收敛速度快,经其优化后的FOPID控制器对负荷扰动的抑制能力要明显优于其他FOPID/PID控制器,并对系统参数变化具有较强的鲁棒性。

LTE基站中PDSCH预映射算法的FPGA实现

提出一种LTE基站中PDSCH资源粒子映射的新设计,其实现是基于FPGA。由于PDSCH的符号级处理过程中,需要对复值调制符号进行缓存,为了最大可能节省FPGA硬件资源,本设计以比特数据代替调制符号进行存储。采用VHDL在Xilinx Kintex-7系列FPGA芯片对本设计进行了验证。

风-光-储和需求响应协同的虚拟电厂日前经济调度优化

目前可再生能源直接并入电网仍然面临稳定性和经济性问题,经过虚拟电厂整合可以缓解对电网的影响。以系统整合后最终运行成本达到最小作为目标,进行新能源出力和负荷在未来24 h的预测,计及电网侧在不同时间内的电价变化情况,采用反向学习的混沌映射自适应粒子群算法对风-光-储能和需求响应不同组合搭配的5种调度方案进行探讨,与原始粒子群算法相比,所提算法可以跳出局部最优解而找到全局最优解。计算结果表明,风-光-储和需求响应都参与供电相比风-光-储供电可以将运行成本降低4.47%,用户舒适度提高3.51%。

基于信息间隙决策理论和电动汽车有序充放电的微电网优化调度

计及区域微电网源-荷两方面不确定性,针对电动汽车(electric vehicles,EV)接入微电网充电所导致的负荷侧波动不确定性问题,提出有序充放电策略进行削峰填谷并构建多目标规划模型;针对新能源机组发电量不确定性问题,引入信息间隙决策理论(information gap decision theory,IGDT)进行模拟分析。首先构建多目标规划函数并对其模糊归一化处理,然后引入改进混沌映射粒子群算法(improved chaos map particle swarm optimization,CMPSO),求得微电网综合运行成本和负荷需求波动量的最优值,最后对经信息间隙决策法优化后的多目标模型进行仿真分析。算例结果验证了优化后的模型对EV入网所带来的负荷波动起到了较好的削峰填谷作用,所采用的IGDT优化模型相较于其他优化方法,能更好地适应严重奈特式不确定性场景,有效协调所搭建模型的鲁棒性与优化调度经济性之间的矛盾。

面向Web科学可视化的临近空间数据视频化方法

临近空间在科学、经济和军事等领域具有重要价值,高效便捷地进行临近空间数据Web科学可视化和信息提取是临近空间数据应用不断深入和扩展的必然需求,但是,已有的临近空间数据类型繁杂和数据量巨大,成为Web传输和实时可视化的关键瓶颈。从数据组织方法角度展开了研究,根据临近空间数据的特点,结合视频压缩原理,提出了将临近空间数据进行分解、插值和色彩空间变换处理形成图像后,选取适宜视频压缩编码方法将图像编码为视频。实验结果表明:所提方法可以实现临近空间数据在Web环境下的高效组织,在保障可视化质量的前提下通过降低数据网络传输量实现了临近空间数据的实时可视化需求。研究结果可以直接解决临近空间数据Web实时可视化问题,将为临近空间科学研究和知识发现提供可视化理论和技术支持,并为类似海量数据Web科学可视化提供方法借鉴。

油膜流场可视化的实时性研究

为了建立虚拟轴承流场分析系统 ,提出基于粒子纹理映射的流场可视化方法 该方法能够充分利用现有的图形硬件加速器 ,具有绘制速度快、图像对比度高、粒子衰减过程易于控制、与流体实验现象一致等特点

基于CS-CPSO与SVM融合的WSNs入侵检测算法

为了提高基本粒子群优化(PSO)算法与支持向量机(SVM)融合的无线传感网络(WSNs)入侵检测算法的检测精度与收敛速度,提出了一种基于完全正弦映射混沌粒子群优化(CS-CPSO)算法与SVM融合的WSNs入侵检测算法(CS-CPSO-SVM)。采用CS-CPSO算法优化SVM参数,不仅将正弦映射混沌搜索应用于粒子群算法中初始种群与局部最优解混沌扰动的产生,且将其用于惯性权重的优化以及随机常数和学习因子的产生,并用多个初始值分别迭代生成多条混沌轨道。以KDDCUP99数据集作为实验数据,经理论分析与仿真实验表明:该方法可以有效地检测入侵行为,并具有良好的检测精度与收敛速度。

Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model

Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.

Parameter identification of the fractional-order systems based on a modified PSO algorithm

In order to better identify the parameters of the fractional-order system,a modified particle swarm optimization(MPSO)algorithm based on an improved Tent mapping is proposed.The MPSO algorithm is validated with eight classical test functions,and compared with the POS algorithm with adaptive time varying accelerators(ACPSO),the genetic algorithm(GA),a d the improved PSO algorithm with passive congregation(IPSO).Based on the systems with known model structures a d unknown model structures,the proposed algorithm is adopted to identify two typical fractional-order models.The results of parameter identification show that the application of average value of position information is beneficial to making f 11 use of the information exchange among individuals and speeds up the global searching speed.By introducing the uniformity and ergodicity of Tent mapping,the MPSO avoids the extreme v^ue of position information,so as not to fall into the local optimal value.In brief the MPSOalgorithm is an effective a d useful method with a fast convergence rate and high accuracy.

Deformation prediction of tunnel surrounding rock mass using CPSO-SVM model

A new method integrating support vector machine (SVM),particle swarm optimization (PSO) and chaotic mapping (CPSO-SVM) was proposed to predict the deformation of tunnel surrounding rock mass.Since chaotic mapping was featured by certainty,ergodicity and stochastic property,it was employed to improve the convergence rate and resulting precision of PSO.The chaotic PSO was adopted in the optimization of the appropriate SVM parameters,such as kernel function and training parameters,improving substantially the generalization ability of SVM.And finally,the integrating method was applied to predict the convergence deformation of the Xiakeng tunnel in China.The results indicate that the proposed method can describe the relationship of deformation time series well and is proved to be more efficient.

基于改进CMPSO和EV有序充放电策略的微电网不确定性研究

针对电动汽车(EV)接入微电网充电导致负荷侧波动问题,引入有序充放电策略构建多目标规划模型研究微电网不确定性和实现微电网经济调度。首先基于EV有序充放电策略构建多目标函数并通过模糊归一化处理成综合单目标函数;然后引入改进混沌映射粒子群算法(CMPSO),求出微电网综合运行成本和负荷需求波动量的最优值;最后通过算例验证了优化后的模型对EV入网带来的负荷波动起到了较好的削峰填谷作用,降低了微电网运行调度费用。所采用的CMPSO算法相较于其他优化算法,大幅度提升了全局搜索能力和收敛精度,能够更优地协调微电网运行稳定性与经济性之间的矛盾。

Turnout fault diagnosis based on DBSCAN/PSO-SOM

In order to diagnose the common faults of railway switch control circuit,a fault diagnosis method based on density-based spatial clustering of applications with noise(DBSCAN)and self-organizing feature map(SOM)is proposed.Firstly,the three-phase current curve of the switch machine recorded by the micro-computer monitoring system is dealt with segmentally and then the feature parameters of the three-phase current are calculated according to the action principle of the switch machine.Due to the high dimension of initial features,the DBSCAN algorithm is used to separate the sensitive features of fault diagnosis and construct the diagnostic sensitive feature set.Then,the particle swarm optimization(PSO)algorithm is used to adjust the weight of SOM network to modify the rules to avoid“dead neurons”.Finally,the PSO-SOM network fault classifier is designed to complete the classification and diagnosis of the samples to be tested.The experimental results show that this method can judge the fault mode of switch control circuit with less training samples,and the accuracy of fault diagnosis is higher than that of traditional SOM network.