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Our analysis of the color indices showed the absence of the known period, 0.d3405 (8.h172), and the presence of several periods at a confidence level of 710 σ, including P0 = 2.h93 (1/P = 8.17c/d) and its alias 2.h64 (1/P = 9.17c/d).


However, we also observe that fractionally integrated hypotheses may be plausible alternatives in the context of structural breaks at a known period of time.


Sampling data are often used to estimate recruitment rates but the latter should be measured, if possible, by exposing uninfected hosts for a known period of time.


On smoothing techniques for the removal of periodic noise of known period


The calculation of the mean y0, the amplitude A, and the phase t0 of the periodic regression function y(t)=y0+(A/2)sin[(tt0)2π/T] is shown for the case of known period length T.

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 A novel repetitive learning control method is proposed for the tracking or rejection of unknown periodic reference or disturbance with a known period. A feedforward compensation is constructed by iterative learning with only the steadystate error to produce asymptotic error convergence. The proposed method is applied to a class of nonlinear systems frequently met in practice, where the disturbances have unknown nonlinear dependency on the output with only continuous Lipschitz condition imposed. With the repetitive... A novel repetitive learning control method is proposed for the tracking or rejection of unknown periodic reference or disturbance with a known period. A feedforward compensation is constructed by iterative learning with only the steadystate error to produce asymptotic error convergence. The proposed method is applied to a class of nonlinear systems frequently met in practice, where the disturbances have unknown nonlinear dependency on the output with only continuous Lipschitz condition imposed. With the repetitive learning controller, the steady state error of the system can be reduced to a very low degree. Compared with the conventional repetitive control method, the proposed method has the merits of high accuracy, easy implementation and being insensitive to nonrepetitive disturbances. Simulation results confirm its effectiveness.  为跟踪或抑制仅周期已知的未知周期参考或扰动信号,提出一种新的重复学习控制方法.利用系统的稳态误差并通过迭代学习构造前馈补偿,实现了误差的渐近收敛.将所提出方法应用于一类常见的扰动信号和系统输出具有未知非线性关系的非线性系统,假设其满足连续里普希斯条件.利用重复学习控制器,系统的稳态误差可以减小到极低的程度.该方法控制精度高,实现简单,与传统的基于时延内模的重复控制方法相比,具有对非重复性干扰不敏感的优点.仿真结果验证了该方法的有效性.  Disturbances acting on a DVD drive servo system contain notable periodic components,which need to be rejected effectively.The periodic disturbances on the focus sero system are mainly caused by skew and thickness odds of a disc.A leadlag compensator is introduced into a DVD focus system to increase the lowfrequency gain and stable margin of the system.Moreover,considering the plant uncertainty of the focus actuator,based on stablity of the system,a basic repetitive controller and a lowpass filter are adopted.The... Disturbances acting on a DVD drive servo system contain notable periodic components,which need to be rejected effectively.The periodic disturbances on the focus sero system are mainly caused by skew and thickness odds of a disc.A leadlag compensator is introduced into a DVD focus system to increase the lowfrequency gain and stable margin of the system.Moreover,considering the plant uncertainty of the focus actuator,based on stablity of the system,a basic repetitive controller and a lowpass filter are adopted.The repetitive controller makes the system reject the periodic disturbances more effectively and track more accurately.The simulation results prove that the repetitive control system is good for rejecting periodic disturbances.  DVD伺服系统遇到的外扰含有明显的周期性成分,要求有行之有效的控制方法抑制之。其中聚焦系统的周期外扰主要由碟片的翘曲和厚度不均匀造成。在DVD聚焦系统中引入超前滞后补偿器以增加系统的低频增益和稳定裕量;在此基础上考虑聚焦机构参数的不确定性,以系统的稳定性为出发点,引入基本重复控制器和低通滤波器,改善了系统对周期扰动的抑制作用并提高了聚焦系统的跟踪精度。仿真结果验证了重复控制系统抑制周期已知的外扰的优越性。  A simple blind estimate algorithm is proposed based on matrix transform.The chip rate of the PN code and PN code period must be known.The received signals are divided into vectors to deal with and cumulate to eliminate the effect of noise and estimate the PN code.The system performances which use different m sequences have been analyzed.The Matlab simulation results show that this algorithm can work well on SNR below18dB.  提出一种基于矩阵变换法的简便盲估计算法.此算法在已知PN码速率及PN码周期已知的情况下,对基带扩频信号进行分段处理和累加运算以消除噪声的影响,进而实现PN码的盲估计,并在此基础上分析了使用不同m序列作扩频序列时的系统性能.Matlab仿真结果表明此算法可以在SNR低于18dB的环境下正常工作.   << 更多相关文摘 
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