In this paper , i use the kalman filtering , the strong tracking filtering and the multiresolution analysis methods to analyze some economic phenomena . further , i compare the new methods with the traditional methods by some examples . then i gain some meaningful research results 论文利用最优估计理论中的卡尔曼滤波方法和强跟踪滤波方法以及小波分析理论中的多尺度分析方法,对一些经济现象进行实时动态分析和多尺度分析,并应用具体实例将新方法与传统方法进行了比较,取得了一些有意义的研究成果。
Results of processing many images of agricultural field and scene of navigation experiments prove that the method based on region segmentation can detect the road correctly , the multi - resolution r oad detection based on wavelet transformation can easily analyze the image on different scaling , and can possess the power to get main contours while suppressing small edges when the scale is large 大量的农田实际环境和实验场景图像处理结果表明,区域的方法可以正确地检测出导航路径,基于小波变换的多分辨率路径检测方法能够方便地实现由粗及精地多尺度分析图像,在大尺度时具有抑制细小的边缘得到景物主要轮廓的能力。
This paper presented m - band wavelet - based watershed image segmentation method for medical digital image . the method was based on a multiresolution application of a m - band wavelet and watershed transformation , followed by a wavelet coefficient based energy computation and region merging procedure . the results showed that the method was useful for the reduction of over - segmentation and can be applied to the segmentation of digital images 本文针对医学数字图像数据量大和噪声情况复杂的特点,结合多尺度分析理论,通过多小波图像分解、能量计算、分水岭变换和区域融合等步骤,克服了分水岭变换严重的过分割问题,实现了有意义的区域分割。
According to the numbers of segmentations , dts has multi scale feature and can reflect different trend similarity of time series under various analyzing frequency . 2 ) an enhanced algorithm , based on dual threshold value , and the conception of sub - series linear are proposed . relative point average error is used to measure the linear degree of sub series , which produced by bottom _ up algorithm 对应时间序列线性分段数目的不同,序列趋势距离具有基于时间的多尺度分析特性,可以有效反应不同分析频率下时间序列的相似程度; 2 )采用相对点平均残差衡量bottom _ up算法划分的子序列线性度,提出子序列线性度概念和一种双误差阀值改进算法,大大提高了趋势序列模型的准确性。
In the space , wavelet approximation formula and sampling formula are gived and these are simple and easy for calculation . it is a new attempt to establish mra in the reproducing kernel space . this not only makes up existing researchful defects but also gives a better method to resolve numerical approximation problems and it further enriches the reproducing kernel theory and wavelet method . this provides more applied method for resolving numerical approximation problems in the space which is often used in the engineering 在再生核空间中建立的多尺度分析方法是利用再生核工具探讨小波理论的又一次大胆的尝试,它不但弥补了现有研究的缺憾,而且给出了解决数值逼近问题的一个新思路,进一步丰富了再生核理论,充实了小波方法,这为在工程中常用的空间中解决数值逼近问题又提供了一种较为实用的方法。
With each domain being looked as distinct material phases and ferroelectrics polycrystal as multiphase material , the coupled macroscopic thermo - electric - mechanical behavior of polycrystalline ferroelectrics can be induced by multi - scale analyze . in response to the applied stimuli , domain can switch and domain switching coverts one variant into another 将铁电体作为非均匀材料,将电畴看作不同的材料相,铁电体宏观尺度的力、电、热多类场的耦合行为,可以通过对其进行多尺度分析得到。
The multiscale sample paths based on distinctive order tree are presented by computer simulation . second , we present the method of 3x3 - order tree - based of redundant multiscale representation for 2 - d markov random fields , and we propose a class of non - redundant multiscale model for reduced - order approximately representing gaussian markov random fields making ties to multiscale analysis 2 、给出了2 - d马尔可夫随机场基于3 3阶树有冗余的多尺度表示方法,并结合多尺度分析的思想,建立了2 - d高斯马尔可夫随机场降阶、近似、无冗余的多尺度表示模型。
Since there are a lot of periodic non - stationary stochastic processes , i combine the kalman filtering and multiresolution analysis methods , and propose the wavelet - kalman filtering hybrid estimating and forecasting method . this method has the real - time , recursion and multiresolution characteristics . in this paper , using this method , i realize the real - time tracking and dynamic multistep forecasting in one cycle 针对自然、社会和经济等环境中存在有大量周期性的非平稳随机过程,我们将kf方法和多尺度分析方法相结合,提出了既具有实时性和递归性又具有多尺度分析能力的小波?卡尔曼滤波混合估计与预报方法,应用此方法可实现周期内对目标状态的实时跟踪估计和动态多步预报。