共查询到18条相似文献,搜索用时 234 毫秒
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针对某2 m望远镜消旋K镜转台,提出了一种基于Hankel矩阵奇异值分解的特征系统实现算法对系统的参数和阶次进行辨识。首先,以正弦扫描信号激励转台并同步采集位置反馈信息,利用谱分析法对测试数据进行分析,得到了系统的频率特性曲线;其次,对系统的Hankel矩阵进行奇异值分解,得到了K镜转台的结构模型;最后,采用特征系统实现算法对Hankel矩阵进行辨识,得到了K镜转台的参数模型。实验结果显示:K镜转台相对均衡的最小阶阶次为6阶,在系统的中低频段获得幅度0.31 dB和相位0.87的辨识精度,相对于参数递阶辨识方法,分别提高了50.7%和23%。结果表明:该方法能够确定一个与系统外特性等价的相对均衡的最小阶状态空间模型,在辨识系统阶次和参数估计方面具有较好的可行性和实用性。 相似文献
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鉴于传统的光电跟踪伺服控制系统频率响应测试方法复杂的缺点,采用了一种全数字化频率响应测量方案对光电跟踪架的频率特性进行测试。提出了一种递阶辨识法与传递函数参数辨识算法相结合进行传递函数辨识的新方法,并推导出了适用于不同阶次传递函数的辨识算法数学模型。在使用递阶辨识法辨识出系统阶次的基础上,利用测试得到的数据,分别采用最小二乘法和以新推导出的数学模型为基础的Levy法、Sanko法和Vinagre法辨识出了光电跟踪架的传递函数。辨识结果表明,新推导出来的数学模型正确,以其为基础的辨识算法误差均小于最小二乘法;3种算法中,Sanko法在整个频域内的辨识效果最好;采用递阶辨识原理与参数辨识算法相结合的方法可行,且精度较高。 相似文献
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参数辨识是过程建模的基础,对于参数辨识问题提出了许多不同的方法.针对传统模型参数辩识方法和遗传算法用于模型参数辨识时的缺点,提出一种基于微粒群优化(PSO)算法的模型参数辨识方法,利用PSO算法的强大优化能力,通过对算法的改进,将过程模型的每个参数作为微粒群体中的一个微粒,利用微粒群体在参数空间进行高效并行的搜索,以获得过程模型的最佳参数值,并将其用于对非线性系统模型的参数辨识,可有效提高参数辨识的精度和效率.该方法应用到实际例子中,获得了满意的辨识精度和效率,得到较为精确的过程模型,模型输出与实际输出基本一致,仿真结果令人满意.实例仿真结果表明,微粒群算法为非线性系统模型参数辨识提供了一种有效的途径. 相似文献
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由于非线性系统输出是其参数的非线性函数,直接利用高阶累积量辨识两层前馈神经网络(FNN)通常是十分困难的。为解决这一问题,该文提出两种基于四阶累积量的FNN辨识方法。第一种方法,FNN的隐元在其输入空间利用多个线性系统近似,进而FNN利用一统计模型混合专家(ME)网络重新描述。基于ME模型,FNN参数可利用统计期望值最大化(EM)算法获得估计。第二种方法,为简化FNN的ME模型,引入隐含观测量。基于隐含观测量估计,FNN被分解为多个单隐元的训练问题,进而整体FNN可利用一两阶层ME描述。基于单隐元的参数估计,FNN可利用一具有更快收敛速度的简化算法获得估计。 相似文献
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为解决雷达辐射源信号分选识别特征评价不够客观和缺乏评价依据等问题,提出了一种基于区间模糊原理、模糊交叉熵和多准则折衷法的特征评价方法. 首先通过区间模糊原理建立信噪比分级评价模型,并基于汉明距离进行寻优得出信噪比权重;其次结合信噪比权重和区间直觉模糊加权平均算子将分级模型整合成群决策矩阵,使用熵最大化法计算属性权重;最后基于多准则折衷法框架,采取模糊交叉熵实现特征方案排序. 仿真实验结果表明,所提方法能够给出与实际仿真实验相一致的分选识别特征评价排序结果,并优于逼近理想点方法,验证了所提方法的可行性和有效性. 相似文献
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Regularized signal reconstruction for level-crossing sampling using Slepian functions 总被引:1,自引:0,他引:1
In this paper, we propose a method for efficient signal reconstruction from non-uniformly spaced samples collected using level-crossing sampling. Level-crossing (LC) sampling captures samples whenever the signal crosses predetermined quantization levels. Thus the LC sampling is a signal-dependent, non-uniform sampling method. Without restriction on the distribution of the sampling times, the signal reconstruction from non-uniform samples becomes ill-posed. Finite-support and nearly band-limited signals are well approximated in a low-dimensional subspace with prolate spheroidal wave functions (PSWF) also known as Slepian functions. These functions have finite support in time and maximum energy concentration within a given bandwidth and as such are very appropriate to obtain a projection of those signals. However, depending on the LC quantization levels, whenever the LC samples are highly non-uniformly spaced obtaining the projection coefficients requires a Tikhonov regularized Slepian reconstruction. The performance of the proposed method is illustrated using smooth, bursty and chirp signals. Our reconstruction results compare favorably with reconstruction from LC-sampled signals using compressive sampling techniques. 相似文献
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This paper presents a novel hierarchical least squares algorithm for a class of non-uniformly sampled systems. Based on the hierarchical identification principle, the identification model with a high dimensional parameter vector is decomposed into a group of submodels with lower dimensional parameter vectors. By using the least squares method to identify the submodels and taking a coordinated measure to address the associated items between the submodels, all the system parameters can be estimated. The proposed algorithm can save the computation cost. The performance analysis indicates that parameter estimates converge to their true values. The simulation tests confirm the convergence results. 相似文献
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This paper derives a Newton iterative algorithm for identifying a Hammerstein nonlinear FIR system with ARMA noise (i.e., Hammerstein nonlinear controlled autoregressive moving average system). This method decomposes a Hammerstein nonlinear system into two subsystems using the hierarchical identification principle, estimating the parameters of the system directly without using the over-parameterization method. The simulation results show that the proposed algorithm is effective. 相似文献
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《Journal of Visual Communication and Image Representation》2014,25(6):1289-1298
We propose a passive multi-purpose scheme for photographic image (PIM) detection and a device class identification method. The motivation for the scheme is the periodicity phenomenon caused by color filter arrays (CFAs) and the demosaicing process. The phenomenon only occurs in the Fourier spectrum in PIMs. The proposed scheme exploits prediction error statistics, local peak detection, and a PIM classifier to analyze the phenomenon for PIM detection. We also develop a hierarchical classifier for device class identification based on the analysis of local peaks in the Fourier spectrum.To evaluate the scheme’s performance, we compile a test dataset of PIMs and PRCG (photorealistic computer graphics) images, and analyze the impact of leak peak detection, JPEG lossy compression, and cropping operations on PIM detection. The accuracy rate of the scheme on 5805 test images is 95.56%, which is higher than that of the methods proposed in Sutthiwan et al. (2009) [1] and Gallagher and Chen (2008) [2]. In addition, for device class identification, the precision rate of the proposed method is at least 93% on Canon, Sony, and Nikon images. The experiment results demonstrate the efficacy of the proposed multi-purpose scheme. 相似文献
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基于模糊逻辑的器件与电路建模技术 总被引:2,自引:0,他引:2
器件与电路的模型是进行CAD的基础,建模在数学上实质是函数逼近问题。模糊逻辑具有多层前向神经网络类似的万能函数逼近特性,本文介绍了利用这一技术进行器件与电路建模的一些尝试。由于模糊逻辑能自然地表达与问题有关的知识与经验,因此有可能取得较神经网络更好的结果。文章介绍了用模糊逻辑建模的过程,提出了用数论网格的样本点生成方法,最后用实例说明了用于器件与电路建模的效果。 相似文献
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This paper presents two blind identification methods for nonlinear memoryless channels in multiuser communication systems. These methods are based on the parallel factor (PARAFAC) decomposition of a tensor composed of channel output covariances. Such a decomposition is possible owing to a new precoding scheme developed for phase-shift keying (PSK) signals modeled as Markov chains. Some conditions on the transition probability matrices (TPM) of the Markov chains are established to introduce temporal correlation and satisfy statistical correlation constraints inducing the PARAFAC decomposition of the considered tensor. The proposed blind channel estimation algorithms are evaluated by means of computer simulations. 相似文献