Fuzzy identification of nonlinear dynamic system based on selection of important input variables

Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the structure.Secondly, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno(T-S) fuzzy modeling.

A new layered space-time detection algorithm for frequency selective fading multiple-input multiple-output channels based on particle filter

In this paper, a new observation equation of non-Gaussian frequency selective fading Bell Labs layered space time （BLAST） architecture system is proposed, which is used for frequency selective fading channels and non-Gaussian noise in an application environment of BLAST system. With othogonal matrix triangularization （QR decomposition） of the channel matrix, the static observation equation of frequency selective fading BLAST system is transformed into a dynamic state space model, and then the particle filter is used for space-time layered detection. Making the full use of the finite alphabet of the digital modulation communication signal, the optimal proposal distribution can be chosen to produce particle and update the weight. Incorporated with current method of reducing error propagation, a new space-time layered detection algorithm is proposed. Simulation result shows the validity of the proposed algorithm.

深度神经网络的测试输入选择与度量标准研究综述

随着深度神经网络在各个领域受到广泛应用,对其进行测试评估并确保其安全性显得尤为重要。测试输入选择方法可以帮助在测试数据集规模较大且标注成本较高的情况下,对测试样本进行选择与排序,以提高测试效率和测试覆盖率。为了深入了解深度神经网络测试输入选择领域的研究进展,对近5年来91篇相关领域的学术论文进行了系统梳理。介绍了深度神经网络测试的基本概念和流程,包括深度学习系统的构建、测试输入的选择和测试结果的评估;概括分析了各种度量标准和测试输入选择方法的适用场景与不足之处,以及彼此之间的相互联系。最后,指出了当前深度神经网络测试输入选择与评估工作面临的挑战和机遇。

考虑场址危险一致性的中国完备条件目标谱与记录选取

即使所处地区的宏观抗震设防烈度相同,不同城市的地震地质构造与活动性背景仍然存在差异。为使地震动输入与所在场址的地震危险性水平保持一致,该研究衔接我国第五代地震区划图的概率地震危险性分析结果,给出了完备条件目标谱的构建方法。采用与第五代地震区划图相同的三级潜源分布和地震动预测方程,进行目标场址的危险性分析与设定地震解耦,衔接三维(震级-经纬度)设定地震解耦结果,给出了考虑所有潜源走向与位置相对贡献的“完备”条件谱。以北京近郊某城市作为算例,结果表明,完备条件谱的均值介于使用长轴和短轴衰减关系的粗略条件谱之间,条件标准差高于粗略条件谱。研究给出的完备条件谱适用于我国区划图的长短轴类型衰减关系,可以很好体现潜源的空间分布特征与背景地震危险性差异。基于我国强震动记录数据库拟合的加速度反应谱相关系数得到的条件谱要略高于基于国外数据库的计算结果。为了实现加速度反应谱均值和标准差的高效匹配,该研究采取多元高斯分布的协方差矩阵近似替换完备条件谱高斯混合分布的协方差矩阵,结合贪心优化算法,选取得到满足目标完备条件谱分布的强震动记录数据集。研究结果系统给出了我国条件目标谱进行记录选取的构建思路与选波方案,可为后续全概率结构性态分析提供依据。

基于最优Copula相关性分析的短期风速预测方法

准确剖析变量间的关联关系,深入挖掘数据的潜在价值,是提升一系列基于统计分析原理所构建风速预测模型精度的关键。为最大程度保留数据的潜在价值并剔除冗余信息,首先将待选输入变量用概率密度函数拟合,其次建立风速与其他变量间的最优Copula函数,再次基于最优Copula函数求解相关系数,明确影响风速预测精度的关键输入变量,最后基于长短期记忆网络模型输出预测结果。基于我国某地区的实测数据集对所提方法进行了验证。实验结果表明,所提出的方法可有效选取关键输入变量,在减少模型训练时间的同时提升预测精度。

生鲜农产品低碳销售渠道选择策略分析

生鲜农产品供应商拥有多种推广销售渠道,不同销售渠道在碳排放上存在差异,生鲜农产品供应商如何选择销售渠道是企业响应低碳号召需面临的问题。因此,构建了单一实体渠道和双渠道销售普通类生鲜农产品、单一渠道和双渠道销售碳标签类生鲜农产品4种策略下的利润函数模型,并分析各策略在利润最大化情况下的最优决策。通过对比分析和案例研究可知:双渠道的开通总能为生鲜农产品供应商带来较高利润,而是否销售碳标签类生鲜农产品,则取决于销售单位碳标签类生鲜农产品与销售单位普通类生鲜农产品所产生的成本差异。

An Upper Limit for Iterative Learning Control Initial Input Construction Using Singular Values

Selecting a proper initial input for Iterative Learning Control (ILC) algorithms has been shown to offer faster learning speed compared to the same theories if a system starts from blind. Iterative Learning Control is a control technique that uses previous successive projections to update the following execution/trial input such that a reference is followed to a high precision. In ILC, convergence of the error is generally highly dependent on the initial choice of input applied to the plant, thus a good choice of initial start would make learning faster and as a consequence the error tends to zero faster as well. Here in this paper, an upper limit to the initial choice construction for the input signal for trial 1 is set such that the system would not tend to respond aggressively due to the uncertainty that lies in high frequencies. The provided limit is found in term of singular values and simulation results obtained illustrate the theory behind.

海洋优势产业甄别的新结构经济学方法——基于Bootstrap的投入产出弹性测算

基于新结构经济学理论,提出了一个具有现实操作意义的海洋优势产业甄别方法。该方法改进了林毅夫从衡量“要素禀赋”入手直接甄别比较优势产业的思路,转从比较优势产业的“技术优势”和“规模优势”两个外在表现入手,进行间接甄别。此外,使用Bootstrap方法克服了中国省级海洋经济数据样本点过少的问题,并以山东省为例进行实证研究。通过与已有研究方法的甄别结果相比较,发现在“简单一致率”“调整一致率”准则下,该识别方法的准确性较高,这说明使用Bootstrap测算的投入产出弹性对甄别海洋比较优势产业具有指导意义。

Factor-graph-based iterative channel estimation and signal detection algorithm over time-varying frequency-selective fading channels

The problem of soft-input so,output （ SISO ） detection for time-varying frequency-selec- tive fading channels is considered. Based on a suitably-designed factor graph and the sum-product al- gorithm, a low-complexity iterative message passing scheme is proposed for joint channel estima- tion, equalization and decoding. Two kinds of schedules （parallel and serial） are adopted in message updates to produce two algorithms with different latency. The computational complexity per iteration of the proposed algorithms grows only linearly with the channel length, which is a significantly de- crease compared to the optimal maximum a posteriori （MAP） detection with the exponential com- plexity. Computer simulations demonstrate the effectiveness of the proposed schemes in terms of bit error rate performance.

Pre-and post-selected measurements with coupling-strength-dependent modulation

Pre-and post-selected(PPS) measurement, especially the weak PPS measurement, has been proved to be a useful tool for measuring extremely tiny physical parameters. However, it is difficult to retain both the attainable highest measurement sensitivity and precision with the increase of the parameter to be measured. Here, a modulated PPS measurement scheme based on coupling-strength-dependent modulation is presented with the highest sensitivity and precision retained for an arbitrary coupling strength. This idea is demonstrated by comparing the modulated PPS measurement scheme with the standard PPS measurement scheme in the case of unbalanced input meter. By using the Fisher information metric, we derive the optimal pre-and post-selected states, as well as the optimal coupling-strength-dependent modulation without any restriction on the coupling strength. We also give the specific strategy of performing the modulated PPS measurement scheme, which may promote practical application of this scheme in precision metrology.

Impact of channel estimation error on channel capacity of multiple input multiple output system

In order to investigate the impact of channel estimation error on channel capacity of multiple input multiple output (MIMO) system, a novel method is proposed to explore the channel capacity in correlated Rayleigh fading environment. A system model is constructed based on the channel estimation error at receiver side. Using the properties of Wishart distribution, the lower bound of the channel capacity is derived when the MIMO channel is of full rank. Then a method is proposed to select the optimum set of transmit antennas based on the lower bound of the mean channel capacity. The novel method can be easily implemented with low computational complexity. The simulation results show that the channel capacity of MIMO system is sensitive to channel estimation error, and is maximized when the signal-to-noise ratio increases to a certain point. Proper selection of transmit antennas can increase the channel capacity of MIMO system by about 1 bit/s in a flat fading environment with deficient rank of channel matrix.

Antenna selection in MIMO radar with collocated antennas

An antenna adjustment strategy is developed for the target tracking problem in the collocated multiple-input multipleoutput(MIMO)radar.The basic technique of this strategy is to optimally allocate antennas by the prior information in the tracking recursive period,with the objective of enhancing the worst-case estimate precision of multiple targets.On account of the posterior Cramer-Rao lower bound(PCRLB)offering a quantitative measure for target tracking accuracy,the PCRLB of joint direction-of-arrival(DOA)and Doppler is derived and utilized as the optimization criterion.It is shown that the dynamic antenna selection problem is NP-hard,and an efficient technique which combines convex relaxation with local search is put forward as the solution.Simulation results demonstrate the outperformance of the proposed strategy to the fixed antenna configuration and heuristic search algorithm.Moreover,it is able to offer close-to performance of the exhaustive search method.

基于神经网络的船舶稳性预报研究

为省略船舶稳性计算中横摇阻尼以及船舶阻力等参数的复杂计算过程,提出一种采用径向基函数(RBF)神经网络对第二代完整稳性失效概率预报的方法.研究包括过度加速度、瘫船稳性以及骑浪/横甩3种失效模式.通过研究船舶相关参数对各失效模式失效概率的影响,确定采用RBF神经网络对每种失效模式进行预报时输入特征的选取,从而降低训练时长.使用训练后的网络对样本船稳性进行预报,采用均方误差和平均绝对百分比误差对预报结果进行评估.对3种失效模式预报结果的平均绝对百分比误差分别为6.49%、7.09%、5.70%,均小于10%,表明采用RBF神经网络可较为精准地对船舶稳性进行预报.

在水域布置中波广播发射天线的可行性分析

本文分析了在水域布置中波广播发射天线的可行性,重点讨论了水位变化对中波天线输入阻抗和调配网络驻波比的影响,为中波发射台场地选择提供了一个新方向。

MIMO场景下的低轨卫星选星算法

低轨(LEO)宽带星座卫星通信作为地面5G无线通信系统的重要补充,始终面临地面可视卫星数量大、传统选星算法计算复杂度高等难题。为实现高效的卫星分组选择算法,基于多输入多输出(MIMO)系统原理,以大尺度路径损耗模型为基础,结合递减卫星选择算法,从而以较低计算复杂度、更快收敛速度有效逼近最优容量性能。该算法在典型LEO星座系统构型下通过数值仿真得到了验证,为未来5G低轨卫星星座通信传输方案设计提供了一种参考思路。

自适应调制MIMO无线系统的物理层安全

针对主动窃听场景下单输入单输出(single-input single-output,SISO)自适应调制频谱效率低和物理层安全(physical layersecurity,PLS)性能差的问题,提出了一种多输入多输出(multiple-input multiple-output,MIMO)安全自适应调制(secure adaptive modulation,SAM)无线系统PLS模型。其设计思路是:通过在发送端采用发送天线选择(transmit antenna selection,TAS)、在合法接收端采用最大比合并(maximal ratio combining,MRC)的天线分集技术来提高主信道的信噪比,以改善频谱效率;同时,限制主动窃听者的误比特率,使之无法解调出主信道发送的机密信息,从而保证合法接收端的PLS。在平均发射功率和PLS的联合约束下,推导SAM-MIMO系统的最佳切换门限和频谱效率以及窃听者的平均误比特率的解析表达式。数值计算与仿真结果表明:与SAM-SISO系统相比,采用TAS/MRC技术的SAM系统既可以提高主信道的频谱效率,又可以确保对主动窃听者的PLS。

毫米波MIMO系统中基于CNN的混合波束成形设计

混合波束成形是毫米波多输入多输出(MIMO)系统中的关键技术,提出了一种基于深度学习的方法来克服复杂性问题且提升系统性能。首先,利用无约束波束成形的相互正交性,对基带波束成形加以正交性的约束后确定等效波束成形器从而通过相位提取来获得模拟波束成形器的相位,将获得的输出插入到基于卷积神经网络(CNN)的混合波束形成(HBCN),HBCN使用的可行集中;其次,HBCN是将天线选择和混合波束成形器设计作为CNN的分类、预测问题,在天线选择上将信道矩阵作为输入,找出最优子阵列,合成的子阵信道矩阵再反馈给CNN来获得模拟和基带波束成形。最后,仿真结果显示,对比传统算法,能够得到更好的频谱效率和更低的复杂度。

大数据发展对企业绿色技术选择的影响研究——基于我国上市公司年报的文本分析

绿色技术进步是绿色经济发展的核心动力,大数据发展对企业绿色技术选择的作用效果不断增强。采用我国2011—2020年上市公司面板数据,通过Python软件对我国上市公司年报进行匹配与抓取,利用变异系数法处理上市公司年报关键词并构建大数据发展综合指标,采用绿色专利规模和绿色专利占比两个指标表征企业的绿色技术选择,实证检验大数据发展对企业绿色技术选择的影响效应。研究发现,大数据发展能够显著优化企业绿色技术选择,绿色投资、智力资本和研发投入是其中的重要作用机制,该影响效应在区域、产权、管理者短视主义等多个维度中存在较为明显的异质性。研究结论在替换核心解释变量、改变样本时间范围、进行工具变量回归等多种稳健性检验中依然成立。研究结论在一定程度上能够为激发大数据潜能和制定绿色生产决策提供重要的参考依据。

地震区划图及抗震规范对强震动选取影响分析

地震区划图及建筑抗震设计规范中的地震动参数对强震动选取有着关键影响。因此从强震动选取的角度,对2010版建筑抗震设计规范,第四代、第五代地震动参数区划图中地震动参数的差异进行提取总结和对比分析。差异主要表现为放大系数平台值、场地条件对特征周期和地震动峰值加速度有不同程度的影响。分析表明,第五代区划图能更真实的表现出地震动的谱型特点,使得建筑抗震设计规范中出现的长周期谱值过高造成的选波困难问题有了较合理的改进;根据场地条件的不同,加入对地震动峰值的调整,可以较为全面的覆盖场地对地震动参数的影响,使得目标谱更具合理性,也使得强震动选取结果更能针对我国不同场地的特定性条件,并能提供较为针对性的进行抗震分析。

基于能量反应的地震动输入选择方法讨论

输入地震波的合理选择是影响结构时程分析结果可信度的重要因素。已有的最优双指标选波方案没有考虑地震动的持时和能量分布的影响。文中考虑地震动持时这一影响结构弹塑性反应的重要因素,以抗震结构的能量反应规律为基础讨论输入地震波选择问题,建议以地震动弹性总输入能反应作为补充指标的选波方案。