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1.
具有传输时延的网络控制系统故障估计与调节   总被引:2,自引:1,他引:2  
冒泽慧  姜斌 《自动化学报》2007,33(7):738-743
In this paper, a method of fault estimation and fault tolerant control for networked control system (NCS) with transfer delay and process noise is presented. First, the networked control system is modeled as a multiple-input-multiple-output (MIMO) discrete-time system with transfer delays, process noise, and model uncertainties. Under this model and under some conditions, a fault estimation method is proposed to estimate the system faults. On the basis of the information on fault estimation and the sliding mode control theory, a fault tolerant controller is designed to recover the system performance. Finally, simulation results are used to verify the efficiency of the method.  相似文献   

2.
In this paper,a method of fault estimation and fault tolerant control for networked control system (NCS) with transfer delay and process noise is presented.First,the networked control system is modeled as a multiple-input-multiple-output (MIMO) discrete-time system with transfer delays,process noise,and model uncertainties.Under this model and under some conditions, a fault estimation method is proposed to estimate the system faults.On the basis of the information on fault estimation and the sliding mode control theory,a fault tolerant controller is designed to recover the system performance. Finally, simulation results are used to verify the efficiency of the method.  相似文献   

3.
Covariance of clean signal and observed noise is necessary for extracting clean signal from a time series.This is transferred to calculate the covariance of observed noise and clean signal’s MA process,when the clean signal is described by an autoregressive moving average (ARMA) model.Using the correlations of the innovations data from observed time series to form a least-squares problem,a concisely autocovariance least-square (CALS) method has been proposed to estimate the covariance.We also extended our work to the case of unknown MA process coefficients.Comparisons between Odelson’s autocovariance least-square (ALS) estimation algorithm and the proposed CALS method show that the CALS method could get a much more exact and compact estimation of the covariance than ALS and its extended form.  相似文献   

4.
Independent component analysis (ICA) is a newly developed promising technique in signal processing applications. The effective separation and discrimination of functional Magnetic Resonance Imaging (fMRI) signals is an area of active research and widespread interest. Therefore, the development of an ICA based fMRI data processing method is of obvious value both theoretically and in potential applications. In this paper, analyzed firstly is the drawback of the extant popular ICA-fMRI method where the adopted signal model assumes the independence of spatial distributions of the signals and noise. Then presented is a new fMRI signal model, which assumes the independence of temporal courses of signal and noise in a tiny spatial domain. Consequently we get a novel fMRI data processing method: Neighborhood independent component correlation algorithm. The effectiveness is elucidated through theoretical analysis and simulation tests, and finally a real fMRI data test is presented.  相似文献   

5.
先验知识与基于核函数的回归方法的融合   总被引:1,自引:0,他引:1  
孙喆  张曾科  王焕钢 《自动化学报》2008,34(12):1515-1521
In some sample based regression tasks, the observed samples are quite few or not informative enough. As a result, the conflict between the number of samples and the model complexity emerges, and the regression method will confront the dilemma whether to choose a complex model or not. Incorporating the prior knowledge is a potential solution for this dilemma. In this paper, a sort of the prior knowledge is investigated and a novel method to incorporate it into the kernel based regression scheme is proposed. The proposed prior knowledge based kernel regression (PKBKR) method includes two subproblems: representing the prior knowledge in the function space, and combining this representation and the training samples to obtain the regression function. A greedy algorithm for the representing step and a weighted loss function for the incorporation step are proposed. Finally, experiments are performed to validate the proposed PKBKR method, wherein the results show that the proposed method can achieve relatively high regression performance with appropriate model complexity, especially when the number of samples is small or the observation noise is large.  相似文献   

6.
基于凸优化算法的无人水下航行器协同定位   总被引:1,自引:1,他引:0  
In this paper, a cooperative localization algorithm for autonomous underwater vehicles (AUVs) is proposed. A ``parallel" model is adopted to describe the cooperative localization problem instead of the traditional ``leader-follower" model, and a linear programming associated with convex optimization method is used to deal with the problem. After an unknown-but-bounded model for sensor noise is assumed, bearing and range measurements can be modeled as linear constraints on the configuration space of the AUVs. Merging these constraints induces a convex polyhedron representing the set of all configurations consistent with the sensor measurements. Estimates for the uncertainty in the position of a single AUV or the relative positions of two or more nodes can then be obtained by projecting this polyhedron onto appropriate subspaces of the configuration space. Two different optimization algorithms are given to recover the uncertainty region according to the number of the AUVs. Simulation results are presented for a typical localization example of the AUV formation. The results show that our positioning method offers a good localization accuracy, although a small number of low-cost sensors are needed for each vehicle, and this validates that it is an economical and practical positioning approach compared with the traditional approach.  相似文献   

7.
In this paper,an important question,whether a small language model can be practically accurate enough,is raised.Afterwards,the purpose of a language model,the problems that a language model faces,and the factors that affect the performance of a language model,are analyzed. Finally,a novel method for language model compression is proposed,which makes the large language model usable for applications in handheld devices,such as mobiles,smart phones,personal digital assistants (PDAs),and handheld personal computers (HPCs).In the proposed language model compression method,three aspects are included.First,the language model parameters are analyzed and a criterion based on the importance measure of n-grams is used to determine which n-grams should be kept and which removed.Second,a piecewise linear warping method is proposed to be used to compress the uni-gram count values in the full languagemodel.And third, a rank-based quantization method is adopted to quantize the bi-gram probability values.Experiments show that by using this compression method the language model can be reduced dramatically to only about 1M bytes while the performance almost does not decrease.This provides good evidence that a language model compressed by means of a well-designed compression technique is practically accurate enough,and it makes the language model usable in handheld devices.  相似文献   

8.
Based on the work in Ding and Ding (2008), we develop a modifi ed stochastic gradient (SG) parameter estimation algorithm for a dual-rate Box-Jenkins model by using an auxiliary model. We simplify the complex dual-rate Box-Jenkins model to two fi nite impulse response (FIR) models, present an auxiliary model to estimate the missing outputs and the unknown noise variables, and compute all the unknown parameters of the system with colored noises. Simulation results indicate that the proposed method is effective.  相似文献   

9.
Parameter identification is a key requirement in the field of automated control of unmanned excavators (UEs). Furthermore, the UE operates in unstructured, often hazardous environments, and requires a robust parameter identification scheme for field applications. This paper presents the results of a research study on parameter identification for UE. Three identification methods, the Newton-Raphson method, the generalized Newton method, and the least squares method are used and compared for prediction accuracy, robustness to noise and computational speed. The techniques are used to identify the link parameters (mass, inertia, and length) and friction coefficients of the full-scale UE. Using experimental data from a full-scale field UE, the values of link parameters and the friction coefficient are identified. Some of the identified parameters are compared with measured physical values. Furthermore, the joint torques and positions computed by the proposed model using the identified parameters are validated against measured data. The comparison shows that both the Newton-Raphson method and the generalized Newton method are better in terms of prediction accuracy. The Newton-Raphson method is computationally efficient and has potential for real time application, but the generalized Newton method is slightly more robust to measurement noise. The experimental data were obtained in collaboration with QinetiQ Ltd.  相似文献   

10.
In this paper,an effective and robust active speech detection method is proposed based on the 1/f process technique for signals under non-stationary noisy environments.The Gaussian 1/f process ,a mathematical model for statistically self-similar radom processes based on fractals,is selected to model the speech and the background noise.An optimal Bayesian two-class classifier is developed to discriminate them by their 1/f wavelet coefficients with Karhunen-Loeve-type properties.Multiple templates are trained for the speech signal,and the parameters of the background noise can be dynamically adapted in runtime to model the variation of both the speech and the noise.In our experiments,a 10-minute long speech with different types of noises ranging from 20dB to 5dB is tested using this new detection method.A high performance with over 90% detection accuracy is achieved when average SNR is about 10dB.  相似文献   

11.
尚方信  郭浩  李钢  张玲 《计算机应用》2019,39(3):874-881
为解决现有无监督图像分割模型对强噪声环境鲁棒性差、无法适应复杂混合噪声的问题,提出了一种基于One-class SVM方法的改进后的噪声鲁棒图像分割模型。首先,基于One-class SVM构建一种数据离群程度检测机制;然后,将离群程度值引入能量泛函,令分割模型可以在多种噪声强度下获得较为准确的图像信息,同时避免现有方法在强噪声环境下,降权机制失效的问题;最后,通过最小化能量函数,驱动分割轮廓向目标边缘演化。在噪声图像分割实验中,当选取不同类型和强度的噪声时,该模型均能得到较为理想的分割结果。在F1-score评估标准下,该模型比基于局部相关熵的K-means(LCK)模型高0.2~0.3,在强噪声环境下具有更高的稳定性,且在分割收敛时间上仅略大于LCK模型0.1 s左右。实验结果表明,所提模型在未显著增加分割耗时的前提下,对于概率、极值及混合噪声均有着更强的鲁棒性,并且可以分割带有噪声的自然图像。  相似文献   

12.
结合核方法的选择性各向异性扩散去噪算法   总被引:1,自引:0,他引:1       下载免费PDF全文
在低信噪比图像噪声抑制处理中,为了有效地保持图像边缘,在基于多相位分层分割算法的各向异性扩散模型的基础上,提出一个基于核方法的选择性各向异性扩散去噪算法。该算法根据图像数据的线性不可分特点,首先利用核方法把多相位分层分割算法中的数据项从线性不可分的低维空间推广到可实现线性可分的高维特征空间,在特征空间中实现图像分割;然后根据分割得到的同质区域的梯度信息改进了P-M模型中的扩散系数;最后,在同质区域中采用改进的P-M模型平滑噪声。实验结果表明,该算法无论在噪声去除还是边缘保持上都具较好的效果。  相似文献   

13.
图像修复的方法有很多种,目前最常用的有基于偏微分方程(PDE)和基于纹理合成的修复方法。在图像的修复和去噪上,偏微分方程都有很好的应用,但对于含有噪声的破损图像的修复,传统的方法是先去除噪声再进行修复。在BSCB模型的基础上加以改进,提出了一种新的修复方法,结合现有的图像修复和图像去噪两种技术的优势,对图像破损区域修复的同时进行整幅图像的去噪,修复和去噪的过程都是各项异性扩散的过程,能很好地保留图像的边缘信息。通过数值实验也表明该方法的有效性。  相似文献   

14.
为了解决基于RGB颜色模型的强噪声彩色图像空域恢复方法的丢失细节问题,依据分解彩色图像得到的三个分量在相关处理后可合成为彩色图像的理论,采用图像的基于像素点的空域处理理论,研究了受强噪声污染的彩色图像的点处理恢复方法。设计了基于RGB颜色模型的彩色图像分量的点处理恢复算法,给出了根据噪声强度调用该算法依次恢复R分量、G分量和B分量的过程。实验结果表明,与经典的空域滤波器比较,基于像素点的空域处理具有保持图像细节的优点,适用于解决基于RGB颜色模型的强噪声彩色图像的恢复问题。  相似文献   

15.
医学图像在重建过程中总会受到噪声干扰,对于此问题,本文提出了一种基于条件生成对抗网络(CGAN)的去噪方法,算法以完整图像作为网络的输入及输出,使生成的图像信息更加稳定可靠。为了适应CT图像的特点,本文对CGAN结构进行了改进,使其能够适应不同噪声水平下的加性高斯白噪声,为了提高效率,在判别器进行训练时采用了损失判别,且在Tensorflow环境下训练网络模型。实验结果表明,与其他传统图像去噪算法相比,本方法能在保留特征信息的同时有效减少图像中的噪声。  相似文献   

16.
对于泊松噪声污染下的模糊共聚焦图像复原问题,为解决传统方法中存在的阶梯效应,提出了一种基于Hessian矩阵范数的正则化方法.在泊松概率模型的基础上,该方法引入Hessian矩阵范数作为正则条件,并应用交替方向乘子法和梯度投影方法求解最优化模型.在激光扫描共聚焦显微镜实验中,所获得的复原图像质量优于传统方法,此结果证明了该方法可以有效地复原泊松噪声污染下的模糊共聚焦图像.  相似文献   

17.
提出一种图像高斯噪声极大似然估计方法,目的是估计出噪声图像所含噪声大小。首先,根据高斯噪声模型的特点,用极大似然法估计噪声值,对图像所含噪声模型进行分析。其次,把噪声图像用直方图表示,从归一化直方图中选出不同的样本观测值,用极大似然算法对噪声的方差进行估计。最后,用MATLAB对该方法进行了模拟实验,实验结果表明此方法所得的图像噪声的方差与实际图像噪声的方差近似相等。所以,此方法无论是在准确性上还是在可行性上均具有优良的特性。  相似文献   

18.
针对传统去噪算法去除含噪声较大的图像时仍有部分噪声残留的问题,本文基于变换域提出一种改进正态逆高斯分布的图像去噪算法。该算法在非下采样剪切波变换域,利用最优线性插值阈值函数改进正态逆高斯模型作为系数分布模型,对高频子带分解系数进行统计建模,以贝叶斯最大后验概率理论实现图像去噪。实验结果表明对于添加不同标准差的高斯白噪声图像,该算法在有效保留图像细节和纹理信息的同时在峰值信噪比方面优于同类去噪算法。  相似文献   

19.
一种基于边缘模式的直方图构造新方法   总被引:6,自引:0,他引:6  
基于图像边缘和噪音模式的分析,使用了一种目标和背景之间的边界检测方法;并基于边界的描述,提出了一种新的在边界两侧和边界内部选取相等数目的像素构造直方图的方法。该种直方图避免现有方法中全部像素直方图、加权直方图和内部像素直方图不适合于小目标的缺点,避免了边缘像素直方图抗噪能力差和阈值因图像边缘类型型变的缺点。该直方图能同时用于大目标和小目标时以及边界是阶跃边缘和屋顶状边缘时的阈值选取,具有很大的通用性和实用性。实验结果证明,使用该方法的直方图优于现有的直方图构造方法。  相似文献   

20.
针对灰度不均匀且含噪声图像的分割问题,提出了全局和局部灰度信息的权重参 数自适应水平集分割模型。首先,利用图像的全局和局部灰度信息构造全局能量项和局部能量 项;然后,利用小波变换和小波阈值去噪方法,构造对噪声不敏感的边缘信息刻画矩阵,定义包含 图像边缘信息的自适应权重系数矩阵;最后,利用定义的权重系数矩阵组合全局和局部能量项, 得到分割模型的能量泛函。使用变分法得到了水平集函数演化方程,利用有限差分法实现数值 求解。实验结果表明,该模型兼有 Chan-Vese 模型和 Local Binary Fitting 模型的优点,能够有效 地分割灰度不均匀含噪图像,并对活动轮廓曲线的初始位置和初始形状具有很强的鲁棒性。  相似文献   

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