Prediction of the residual strength of clay using functional networks

Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of stability of slopes or landslides. This effect is more pronounced in sensitive clays which show large changes in shear strength from peak to residual states. This study analyses the prediction of the residual strength of clay based on a new prediction model, functional networks（FN） using data available in the literature. The performance of FN was compared with support vector machine（SVM） and artificial neural network（ANN） based on statistical parameters like correlation coefficient（R）, Nash–Sutcliff coefficient of efficiency（E）, absolute average error（AAE）, maximum average error（MAE） and root mean square error（RMSE）. Based on R and E parameters, FN is found to be a better prediction tool than ANN for the given data. However, the R and E values for FN are less than SVM. A prediction equation is presented that can be used by practicing geotechnical engineers. A sensitivity analysis is carried out to ascertain the importance of various inputs in the prediction of the output.

Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network

Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.

基于功能依赖网的评估指标体系构建

评估指标体系是用户针对评估问题建立的解决评估问题的概念模型,在作战效果评估中直接体现军事训练评估的需求,构建兼具系统性和代表性的作战能力指标体系对作战能力的评估具有重要的意义。从部队作战的军事需求和评估指标构建的实际情况出发,引入功能依赖网理论,对评估指标体系进行结构性改进,将传统树形结构的指标体系改进为网络化结构,弱化了评估指标独立性的要求,增加了节点和节点之间的容错性,更好地体现了评估指标间的约束关系,做到评估指标体系的有效、可测以及协调,对训练过程和结果进行全面、动态、准确的评估和实时反馈,真正发挥演习训练的作用。

基于加权相位滞后指数热力图的脑力负荷识别

脑力负荷识别对提高人机交互操作人员的工作绩效具有重要意义。目前已有研究表明,通过提取脑电(electroencephalogram,EEG)信号的能量特征进行脑力负荷识别取得了不错的分类效果。但该方法只关注到信号的幅度信息,而忽略了相位信息;只分析了各个通道的频域特征,没有考虑不同通道信号之间的同步关系。为充分考虑不同脑区间的功能连接性,提出一种基于加权相位滞后指数(weighted phase lag index,WPLI)热力图的脑力负荷分类方法。对预处理后的脑电信号计算两两通道间的WPLI并绘制热力图,用于评估不同通道信号之间的相位耦合情况,由此反映不同脑区间的功能连接性。由WPLI热力图可以直观地观察到:在高、低负荷状态下,大脑功能连接性的分布存在明显差异。通过实验分别对能量特征图和WPLI热力图采用方向梯度直方图-支持向量机(histogram of oriented gradient-support vector machine,HOG-SVM)和LeNet-5两种方法进行分类。结果表明:WPLI热力图和LeNet-5的组合具有较好的分类结果。

基于ISOA-SVR模型的短期网络舆情预测

网络舆情传播具有时效性和小样本特征,提出一种改进海鸥算法优化支持向量回归的网络舆情预测模型ISOA-SVR。为提高SOA算法的性能,设计sigmoid函数非线性收敛因子实现种群迁徙与攻击阶段的平滑转换;引入精英个体多阶段动态扰动避免局部最优;设计正余弦优化指引种群位置二次更新,提高局部寻优能力。SVR学习效率高、逼近能力强,但对参数初值敏感、泛化能力仍有不足,利用ISOA算法对SVR优化调参,构建网络舆情预测模型ISOA-SVR。实验结果表明,ISOA-SVR数据拟合度更高,稳定性和收敛性表现更好。

时空角度下极端天气的可达性指标比较

为了确定不同可达性指标在极端天气下的性能,基于Hansen可达性指标,结合飓风“桑迪”期间纽约市曼哈顿区的出租车行程数据,从吸引系数和阻抗函数角度比较5类可达性指标,从时间和空间维度分析曼哈顿区的灾前、灾中、灾后可达性.比较结果表明,需求吸引系数体现灾害对出行距离的影响,比人口吸引系数更适合描述灾害场景的出租车流量变化特征;3类阻抗函数可达性的皮尔逊相关系数绝对值均大于0.8,但可达性的变化幅度不同;相较于累积机会型阻抗函数,使用重力型或混合型阻抗函数评估的灾害下可达性水平更符合现实情况.实验结果显示,使用需求吸引系数和重力型阻抗函数分析可达性能够体现灾害不同阶段的出租车流量和行程时间变化特征,极端天气对城市路网的影响存在显著时空异质性.确定可达性指标的适用条件有助于评估灾害下的路网性能和指导灾后重建工作.

基于计算机视觉的石化火灾智能监测研究

针对火灾现场干扰因素复杂、传统火灾报警方式相对滞后的问题,提出了一种基于YOLOv5改进的火灾识别算法。首先,通过改进激活函数提升模型非线性表达及泛化能力,改进损失函数,减少模型对非必要特征的学习;然后,通过引入注意力机制CBAM,增强模型在通道和空间的双向感知力;最后,设计改进YOLOv5算法与其他识别算法对比实验。通过定性及定量分析证明改进算法的有效性,与原YOLOv5算法相比,模型平均精准率AP提高了5.85%,整体网络性能有了较明显的提升,满足了火灾图像检测准确度、实时性的需求,测试结果证明其具有良好的性能以及应用价值。

基于Copula函数模型的计算机网络可靠性预测方法

由于计算机网络组件之间的依赖关系无法有效捕捉,导致计算机网络可靠性预测时间延迟较长。对此,设计一种基于Copula函数模型的计算机网络可靠性预测方法。从起始节点出发,通过优先搜索逐步扩展至相邻节点,直至到达目标节点,确定网络的最小路集。运用Copula函数模型有效捕捉多个网络组件之间的依赖关系,设置可靠性指标。基于这些可靠性指标和最小路集信息,构建一个计算机网络可靠性预测模型。该模型不仅考虑了网络结构的特点,还融合了历史运行数据,从而能够实现对计算机网络可靠性的准确预测。实验结果表明,设计的基于Copula函数模型的计算机网络可靠性预测方法,平均预测时间延迟仅0.27 s,优势显著,表明该方法能够在较短的时间内完成预测任务,且预测结果可靠。

基于脑功能网络的抑郁脑电信号分类方法

通过探究多种脑功能网络构建方法在抑郁脑电信号分类中的性能,提出了一种基于相位滞后指数、自适应阈值和卷积神经网络的高精度抑郁脑电信号分类方法。首先,利用不同连通性方法和二值化方法的组合构建了多种抑郁脑功能网络;然后,基于图论分析从脑功能网络中提取网络参数;最后,将脑网络参数输入多种分类器,实现抑郁脑电信号分类。此外,建立了一种基于可分性测度和量子粒子群优化的自适应阈值法和自适应密度法,以避免人工设置阈值和网络密度的主观性。实验结果表明:与其他常用方法相比,在alpha频带上,所提分类方法的性能最好,分类准确率和灵敏度分别为88.03%、89%。

An Adaptive RBF Neural Network Control Method for a Class of Nonlinear Systems

This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively.The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives(CMD) system, which satisfies the structure of nonlinear system,is taken for simulation to confirm the effectiveness of the method.Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.

Dynamic cluster member selection method for multi-target tracking in wireless sensor network

Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a special cluster member(CM) node selection method is put forward in the scheme.An energy efficiency model was proposed under consideration of both energy consumption and remaining energy balance in the network.A tracking accuracy model based on area-sum principle was also presented through analyzing the localization accuracy of triangulation.Then,the two models mentioned above were combined to establish dynamic cluster member selection model for MTT where a comprehensive performance index function was designed to guide the CM node selection.This selection was fulfilled using genetic algorithm.Simulation results show that this method keeps both energy efficiency and tracking quality in optimal state,and also indicate the validity of genetic algorithm in implementing CM node selection.

在线优化参数的无模型预测神经网络自抗扰控制

关于难以建模的非线性系统的控制问题,提出具有辅助变量的全格式动态线性化方法逼近非线性系统模型,基于其构建系统的预测模型,给出采用直接极小化指标函数自适应优化算法的参数估计算法,在扩张状态观测器中引入控制输入的微分项,并将控制输入和其微分的系数改进为关于观测状态的函数,因其未知,使用RBF神经网络逼近,利用非线性递推最小二乘法同时优化RBF神经网络参数和自抗扰控制器参数,综上研究提出在线优化参数的无模型预测神经网络自抗扰控制算法。仿真研究验证了上述研究的合理性和有效性,系统响应精度高。

A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm

Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm(DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly,the information processing strength(IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm(DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIASORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.

A New Robust Adaptive Neural Network Backstepping Control for Single Machine Infinite Power System With TCSC

For a single machine infinite power system with thyristor controlled series compensation(TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network(RBFNN). The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error,which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function,which can guarantee the uniform ultimate boundedness(UUB) of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.

基于无人机多光谱遥感的芳樟矮林SPAD反演

为实现利用多光谱技术开展芳樟叶绿素相对含量(SPAD)监测,及时快速诊断芳樟矮林生长状况,为田间管理决策提供信息支持,以红壤区芳樟矮林为研究对象,利用无人机多光谱遥感影像,提取波段反射率,筛选植被指数,分别以波段反射率和植被指数为模型输入量,采用偏最小二乘回归、支持向量回归、反向传播(Back propagation,BP)神经网络和径向基函数(Radial basis function,RBF)神经网络4种方法构建芳樟矮林SPAD反演模型,并对比不同输入量、不同模型模拟结果的反演精度。研究结果表明:对比两种不同的输入量,在同一模型反演的精度相差不大;其中,基于偏最小二乘回归法,以植被指数为模型自变量估测芳樟矮林SPAD效果略优;基于支持向量回归、BP神经网络和RBF神经网络,以波段反射率为模型自变量估测芳樟矮林SPAD效果略优;对比4种建模方法,不同方法建模预测精度不同,与偏最小二乘回归、支持向量回归和BP神经网络相比,基于RBF神经网络反演芳樟SPAD的精度最高,以波段反射率和植被指数为模型输入量的测试集为例,其决定系数R^(2)分别为0.788、0.751,均方根误差(RMSE)分别为1.838、2.457,表明RBF神经网络在芳樟矮林SPAD预测过程中具有明显优势。

基于FY-3D/MERSI数据的东北地区干旱监测方法研究

干旱是影响东北地区粮食安全的主要农业气象灾害之一,遥感技术是一种可便捷进行大范围干旱监测的手段。针对目前遥感干旱指数在作物生长发育过程中监测干旱的局限性和适用性等问题,以东北地区玉米和大豆等主要大田作物发育期为切入点,基于FY-3D/MERSI卫星遥感数据和地面土壤相对湿度实测数据,开展不同作物发育阶段干旱监测指数适用性分析,结合径向基神经网络方法,构建全时期和分时期土壤相对湿度反演模型,利用实测土壤相对湿度数据开展精度验证与对比分析。结果表明:风云三号MERSI传感器数据在干旱监测中具有可行性,表观热惯量(ATI)在低植被覆盖或裸土时效果较好,适用于作物冻土期、裸土期和播种~拔节期;水分指数(WI)适用于播种~拔节期、拔节~抽雄期和成熟期等植被生长时期;分时期土壤相对湿度反演模型精度高于全时期土壤相对湿度反演模型,前者监测精度在80.0%以上,比全时期模型精度提高了10%~25%,尤其在冻土期(3月),分时期模型反演精度达到了92.6%。基于作物生长时期和形态差异,选择最适宜遥感干旱指数建立分时期土壤相对湿度反演模型,提高了干旱监测的准确性和可靠性。

基于RBF神经网络的BDS接收机作战效能评估

针对传统作战效能评估方法存在主观性强,依赖专家经验等问题,提出一种基于径向基函数(RBF)神经网络的北斗卫星导航系统(BDS)接收机作战效能评估方法:梳理BDS接收机作战效能评估需求,并构建BDS接收机作战效能评估指标体系;然后对网络基础原理、学习算法和评估流程进行研究。实验结果表明,提出的方法能够有效完成BDS用户机的作战效能评估,虽然RBF神经网络在收敛速度上比反向传播(BP)神经网络要慢5.52倍,但是损失函数和准确率相比BP神经网络分别提升了65.7%和8%,而且与传统的装备作战效能评估算法相比,评估结论更加客观,具有一定实用性。

基于链路预测的脑功能网络重构方法及应用

【目的】传统的静息态功能磁共振成像脑网络构建方法可能存在虚假链接或者缺失边,网络表达不够准确,重复测量的稳定性有待进一步提高。【方法】基于3种局部信息指标和4种节点定义策略,将链路预测算法用于脑功能网络重构,并利用两组测试数据对重构网络拓扑指标的可靠性进行分析。【结果】实验结果表明,重构网络提高了重复测量的稳定性。此外,利用重构网络对阿尔兹海默症(alzheimer’s disease, AD)患者脑网络进行组间差异分析,结果发现重构网络存在显著的组间差异,符合已有研究结果。

多激活函数的立体神经网络设计

合适的激活函数和参数可大幅提高神经网络预测的准确率,因此,工作人员会耗费大量时间和精力对激活函数进行多轮对比测试,通过评价指标的表现做出最终选择。为减少这种无效科研工作时间,建立了激活函数池,将传统神经网络进行叠加,形成了立体神经网络。该网络可对激活函数池中的多个函数同时进行训练,自动调整参数,通过ROC和AUC对各个维度的网络进行评价,选出主网络对未知数据进行预测。该立体神经网络可以帮助工作人员节省大约25%的手工比对和调整参数时间,预测准确率与使用正确激活函数的传统神经网络持平。

多变量无模型预测神经网络线性自抗扰控制

对难以建模的多变量非线性系统的控制难题,提出改进的具有辅助向量的多变量全格式动态线性化方法,采用其逼近非线性系统,用其构成预测模型,将其转化为具有耦合的若干个子系统,利用直接极小化指标函数自适应优化算法辨识其参数,将多变量线性扩张观测器的线性控制输入项改进为关于观测状态和控制输入向量及其微分的向量函数,并由该向量函数的逆向量函数构建当前控制输入向量,因其未知,使用对角回归神经网络逼近控制输入向量函数,采用多变量非线性递推最小二乘法优化对角回归神经网络连接权及多变量线性自抗扰控制参数,综上研究提出在线优化参数的多变量无模型预测神经网络线性自抗扰控制算法。仿真研究表明系统响应精度高,性能好,优于传统的线性自抗扰控制算法。