基于自适应FLP滤波的金属谐振陀螺信号处理方法

针对传统前向线性预测(FLP)滤波在金属谐振陀螺信号处理中,无法解决收敛速度和稳态误差之间矛盾且动态滤波效果较差的问题,提出了一种基于自适应FLP滤波信号处理方法。首先采用Allan方差对陀螺原始信号进行分析,得出不同噪声占比;然后采用自适应FLP滤波方法,通过建立补偿因子与估计误差的非线性关系,实时调整步长因子大小,修正滤波误差;最后用自研的金属谐振陀螺惯性系统试验验证该方法的有效性。试验结果表明:自适应FLP滤波方法可抑制量化噪声、角度随机游走和零偏不稳定性等。降噪效果比传统FLP滤波方法更为明显,收敛速度更快,动态时自适应FLP滤波方法降噪效果是传统FLP滤波方法的1.95倍,该方法可有效提高金属谐振陀螺的性能。     

基于自适应神经模糊推理系统的陀螺建模方法

系统地介绍了近年来陀螺信号处理中出现的各种主要的滤波方法,并分析了各自的优缺点.针对陀螺输出信号自身的特点,提出了一种基于自适应神经模糊推理系统的陀螺滤波方法,建立了陀螺误差模型.提出的滤波方法同时具有神经网络与模糊逻辑推理的优点,从而有效地减小了建模误差.分别探讨了陀螺静态时的零偏信号估计模型与敏感动态信号时的滤波方案.由仿真结果可以看出,该方法可以有效地估计陀螺的零偏,并能抑制陀螺输出信号中的噪声.     

一种基于Haar小波的光纤陀螺信号实时滤波方法

为了改善惯性稳定平台控制系统中光纤陀螺信号质量,提出了一种基于Haar小波的光纤陀螺信号实时滤波方法。首先通过仿真,说明了影响现有小波方法实时滤波效果的根源,即边界问题;进而通过理论分析,对边界问题产生的原因进行深入剖析,并从支撑集和对连续阶梯信号的逼近两个层面,详细论证了Haar小波对于光纤陀螺信号实时滤波的适用性;最后提出一种基于Haar小波的实时陀螺信号滤波方法,既利用了小波优越的信号-噪声分离能力,又可满足实时应用背景的要求,完全适用于惯性稳定平台控制系统中的光纤陀螺信号滤波。经试验验证,此方法具备很好的陀螺实时滤波性能,滤波后信号的随机噪声可降低40%以上,使用滤波后陀螺信号闭环后,惯性稳定平台控制系统的稳定精度可以提升14%。     

基于前向线性预测算法的光纤陀螺零漂的神经网络建模

在详细分析光纤陀螺零漂的基础上,提出了先用滤波算法对光纤陀螺信号进行预处理,然后采用RBF神经网络对滤波后的信号进行建模的方法。针对光纤陀螺信号特点分别采用FLP算法、小波滤波算法、解相关变步长LMS自适应滤波算法对其进行了预处理,比较三种滤波方法,小波滤波算法效果优于其它两种预处理方法,但针对基于预处理后的陀螺信号采用RBF神经网络进行建模时,小波滤波预处理后的信号在建模精度上却是最差的,而对FLP算法滤波后的信号进行RBF建模,建模精度提高了两个数量级。结果表明:基于FLP算法的RBF神经网络在光纤陀螺中的建模是有效的,可大大提高建模的精度。     

模糊抗差自适应粒子滤波及其在组合导航中的应用

在研究模糊控制理论的基础上,吸收了粒子滤波、自适应滤波和抗差估计的优点,提出一种新的模糊抗差自适应粒子滤波算法。文中根据量测向量中的粗差对状态向量滤波的影响,建立抗差自适应粒子滤波模型,获得自适应因子,然后对滤波处理后的数据残差基于模糊理论构造等价权函数,利用等价权函数和自适应因子合理地分配信息,因而可以达到一般滤波方法无法达到的滤波精度,且能有效地控制粗差对导航解的影响。最后,将该算法应用到组合导航系统中,并进行仿真验证。仿真结果证明,文中提出的模糊抗差自适应粒子滤波算法的滤波精度相对于扩展Kalman滤波和粒子滤波提高了3至5倍,明显提高了导航定位精度,且计算简单,便于实时性计算。     

分形滤波在高精度海洋重力仪数据处理中的应用研究

为有效地消除各种外界干扰噪声对高精度海洋重力仪测量值的影响,提高重力异常测量值的精度,在分析基于分形理论的滤波算法基础上,首次将其应用到高精度海洋重力仪系统数据处理中,并与自适应卡尔曼滤波进行对比分析,以实际信号数据与处理后信号数据的均方差作为衡量两种数据处理方法好坏的依据。理论分析和仿真实验表明:分形滤波方法和自适应卡尔曼滤波都能在一定程度上消除干扰噪声对重力异常信号的影响,但在相同背景条件下,分形滤波的性能优于自适应卡尔曼滤波。     

VOGL-BP网自适应Kalman滤波器在导航中的应用

针对当前自适应Ｋａｌｍａｎ 滤波在导航中的应用存在滤波不稳定和对导航精度的提高幅度有限的现状,本文首次提出了将ＢＰ 神经网络运用到Ｋａｌｍａｎ 滤波器中来形成ＶＯＧＬ－ ＢＰ网自适应Ｋａｌｍａｎ 滤波器来提高滤波的稳定性和滤波的精度。经过大量的模拟实验证明,该方法是切实可行的,能有效地提高滤波的稳定性和精度。     

量测噪声有限记忆在线估计简化算法

量测噪声有限记忆在线估计方法通过对新息序列的实时统计计算,更新系统量测噪声阵 R,增强了滤波器的自适应能力。但量测噪声有限记忆在线估计方法需要在每个滤波周期内对量测噪声阵 R 进行估计并更新统计周期内的量测新息,存在着信息统计与数据更新计算量大的不足。针对此问题,提出了一种基于协方差匹配技术的自适应滤波算法,将协方差匹配技术与量测噪声有限记忆在线估计方法相结合,根据协方差匹配结果,选择性统计量测噪声阵 R。仿真结果表明,简化算法可以在保证滤波精度相当的前提下,减小计算量,提高实时性。     

基于QR分解的最小二乘格型自适应滤波算法的噪声主动控制方法研究

本文提出了格型联合估计滤波器结构与基于QR分解的最小二乘格型(QRD-LSL)自适应滤波算法相结合的噪声控制方法,该方法对联合估计过程进行了改进并得到了基于各阶估计误差的联合过程估计权系数更新关系,格型联合估计器结构简单,QRD-LSL自适应滤波算法数值稳定性好.采用实测信号进行仿真,仿真结果表明本文提出的噪声控制方法有良好的噪声控制效果,收敛速度快,计算量小,稳态误差小,跟踪性能好.     

基于自适应时频峰值滤波的光纤陀螺去噪算法

为减小光纤陀螺输出信号噪声、提高惯导系统精度,提出了光纤陀螺信号自适应时频峰值滤波算法。对光纤陀螺信号进行初始变换并调制,采用伪Wigner-Ville分布对调制信号进行时频分析,给出了一种自适应的伪Wigner-Ville分布最优窗长获取准则,通过局部峰值搜索实现编码信号的瞬时频率估计进而还原出有用信号,实现了光纤陀螺噪声的去除。详细对比了小波方法与自适应时频峰值滤波算法并分析了两者的去噪效果。仿真结果和实际数据验证表明:自适应时频峰值滤波算法能有效减小光纤陀螺输出噪声,信噪比比小波滤波改善1³ dB;特别对于高动态信号,该算法滤波后的信号能够有效地跟踪原始信号。     

基于人工神经网络的亚格子应力建模

亚格子(SGS)应力建模在湍流大涡模拟(LES)中有着极为重要的作用. 传统亚格子应力模型存在相对误差较大、耗散过强等问题. 近年来, 计算机技术的发展使得人工神经网络(ANN)等机器学习方法逐渐成为亚格子应力建模型的新研究范式. 本文着重考虑滤波宽度及雷诺数影响, 在不可压缩槽道湍流中建立了亚格子应力的ANN模型. 该模型以滤波后的直接数值模拟(fDNS)流场物理量及滤波尺度为输入信息, 相应滤波尺度下的亚格子应力为输出量. 通过对不同滤波尺度及不同雷诺数数据的训练, ANN模型能够给出与直接数值模拟(DNS)高度吻合的亚格子应力. 此外, 模型在亚格子耗散等非ANN建模量上也有着优异的预测性能, 与基于DNS获得的对应物理量的相关系数大都在0.9以上, 较梯度模型及Smagorinsky模型有明显提升. 在后验测试中, ANN模型对流向平均速度剖面的预测同样优于梯度模型、Smagorinsky模型及隐式大涡模拟(ILES)等传统LES模型. 在脉动速度均方根预测方面, 除了某些法向位置外ANN模型的性能整体上相对其他3个模型有所提升. 然而, 随着网格尺度的增大ANN模型预测的结果与fDNS结果的偏差逐渐增大. 总之, ANN方法在发展高精度亚格子应力模型上具有很大的潜力.      

CNN/Kalman技术在组导中的应用研究

经典的卡尔曼滤波器要求假设系统的动态模型和观测模型的噪声统计特性已知，而组合导航系统的噪声具有非先验性。为了解决这一问题，提出了一种新型复合神经网络(CNN)辅助卡尔曼滤波器(Kalman)。仿真试验结果表明：该辅助算法的精度与一般卡尔曼算法相比提高了2倍，收敛时间缩短近200s，并有效地克服了传统神经网络学习速度慢、泛化能力弱的缺点，使系统具有自适应能力以应付动态环境的扰动。     

基于Fuzzy ARTMAP神经网络的景像匹配实时图选取方法

提出一种新的基于Fuzzy ARTMAP神经网络并利用图像直方图特征的快速景像匹配实时图选取方法。与已有的方法相比,该方法充分考虑了图像的边缘、亮度、对比度、信噪比等特征对影像实时图质量的影响,具有自适应聚类、收敛导速,实时性好,分类准确率高和通用性强等优点。将该方法应用于SMGS（景像匹配制导系统）进行实时图像的自动选取,可大大提高SMGS的智能性,可靠性和实时性。     

GA based meta-modeling of BPN architecture for constrained approximate optimization

Artificial neural networks (ANN) have been extensively used as global approximation tools in the context of approximate optimization. ANN traditionally minimizes the absolute difference between target outputs and approximate outputs, thereby resulting in approximate optimal solutions being sometimes actually infeasible when it is used as a meta-model for inequality constraint functions. The paper explores the development of the modified back-propagation neural network (BPN) based meta-model that ensures the constraint feasibility of approximate optimal solution. The BPN architecture is optimized via genetic algorithm (GA) to determine integer/continuous decision parameters such as the number of hidden layers, the number of neurons in a hidden layer, and interconnection weights between layers in the network. The verification of the proposed approach is examined by adopting a number of standard structural problems and an optical disk drive (ODD) suspension problem. Finally, GA based approximate optimization of suspension with optical flying head (OFH) is conducted to enhance the shock resistance capability in addition to dynamic characteristics.     

Spatial artificial neural network model for subgrid-scale stress and heat flux of compressible turbulence

The subgrid-scale(SGS) stress and SGS heat flux are modeled by using an artificial neural network(ANN) for large eddy simulation(LES) of compressible turbulence. The input features of ANN model are based on the first-order and second-order derivatives of filtered velocity and temperature at different spatial locations. The proposed spatial artificial neural network(SANN)model gives much larger correlation coefficients and much smaller relative errors than the gradient model in an a priori analysis. In an a posteriori analysis, the SANN model performs better than the dynamic mixed model(DMM) in the prediction of spectra and statistical properties of velocity and temperature, and the instantaneous flow structures.     

机器学习在求解一维双曲守恒律方程中的应用

双曲守恒律方程对空气动力学、物理学和海洋学等众多领域问题的计算有着重大意义,本文应用机器学习框架下的BP神经网络对双曲守恒律方程近似求解.首先,采用熵稳定格式及基于自适应移动网格的熵稳定格式所得多个时间层的数值解构造网络输入,采用高分辨率熵稳定格式所得对应的多个时间层的数值解构造网络输出,并对数据集作归一化处理.随后,...     

Modeling and prediction of chaotic systems with artificial neural networks

This study of chaotic systems and their prediction is motivated by the fact that many phenomena, both natural and man‐made, are of a chaotic nature. Such phenomena include but are not limited to earthquakes, laser systems, epileptic seizures, combustion, and weather patterns. These phenomena have previously been thought to be unpredictable. However, it is indeed possible to predict time series generated by chaotic systems. The primary objective of this study is to develop a system that would train the artificial neural network (ANN) and then predict the future data of the process. In the present application, the chosen chaotic data set was obtained by solving Lorenz's equations. To predict the future data, the concept of a multilayer feed‐forward ANN with nonlinear auto‐regressive moving averages with exogenous input is used. A Backpropagation algorithm is used to train the network for the chaotic data. The final updated weights from the trained network were then used for the prediction of the future values of the system. Lyapunov exponents, phase diagrams and statistical analyses were used to evaluate the neural network output. A correlation of 94% and a negative Lyapunov exponent indicate that the results obtained from ANN are in good agreement with the actual values. Copyright © 2009 John Wiley & Sons, Ltd.     

Combined neural network and reduced FRF techniques for slight damage detection using measured response data

Summary  This paper deals with structural damage detection using measured frequency response functions (FRF) as input data to artificial neural networks (ANN). A major obstacle, the impracticality of using full-size FRF data with ANNs, was circumvented by applying a data-reduction technique based on principal component analysis (PCA). The compressed FRFs, represented by their projection onto the most significant principal components, were used as the ANN input variables instead of the raw FRF data. The output is a prediction of the actual state of the specimen, i.e. healthy or damaged. A further advantage of this particular approach is its ability to deal with relatively high measurement noise, which is a common occurrence when dealing with industrial structures. The methodology was applied to detect three different states of a space antenna: reference, slight mass damage and slight stiffness damage. About 600 FRF measurements, each with 1024 spectral points, were included in the analysis. Six 2-hidden layer networks, each with an individually-optimised architecture for a specific FRF reduction level, were used for damage detection. The results showed that it was possible to distinguish between the three states of the antenna with good accuracy, subject to using an adequate number of principal components together with a suitable neural network configuration. It was also found that the quality of the raw FRF data remained a major consideration, though the method was able to filter out some of the measurement noise. The convergence and detection properties of the networks were improved significantly by removing those FRFs associated with measurement errors. Received 9 March 2000; accepted for publication 12 December 2000     

红板岩材料隐式本构模型的建立及其应用研究

针对红板岩材料在岩土工程中所表现的大量模糊的和不确定的因素等特点,基于人工神经网络的学习能力,借助于室内岩石力学试验,进行了对该材料的力学本构特性进行了神经网络模拟研究,提出了隐式本构模型的思想和方法,并通过该方法对该岩石的流变试验结果进行学习,获得了以网络权值结构保存的力学特性知识,由此得到了表征红板岩应力应变本构关系的隐式本构模型。应用结果表明,该方法对岩土类材料本构关系的模拟研究具有很好的应用前景。     

改进遗传人工神经网络在组合导航中的应用

鉴于常规卡尔曼滤波算法组合导航系统数据融合算法中,存在易于发散的缺陷,尝试将遗传优化人工神经网络引入组合导航系统中.针对传统遗传算法存在的易早熟、算法稳定性差、固定的交叉和变异概率影响收敛效果等缺点,采用浮点式编码方式,两两竞争的选择策略、引入突变操作、重新定义交叉算子和自适应的交叉变异算子等措施进行了遗传算法的改进.仿真结果表明,改进后的算法更为有效,并且精度与常规卡尔曼滤波算法相当.