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wavelet相关的网络例句

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The topics are divided into eight chapters and two appendices, is to learn from the preamble of Chapter 1 to Chapter 7 of the preparation of knowledge, that is, inner product space Chapter 1 on the basis of Fourier series of knowledge Chapter 2 on Fourier Transform Chapter 3 introduced the Discrete Fourier Transform and Fast Fourier Transform Chapter 4 to Chapter 7 to discuss wavelet, with emphasis on construction of orthogonal wavelet appendix describes slightly complex themes, as well as a number of technical concepts or produce demo MATLAB graphics code

全书分为8章和2个附录,前言部分是学习第1章至第7章的准备知识,即内积空间;第1章讲解傅里叶系列的基础知识;第2章讲解傅里叶变换;第3章介绍离散傅里叶变换以及快速傅里叶变换;第4章至第7章讨论小波,重点在于正交小波的构建;附录部分则介绍稍微复杂的一些技术主题以及演示概念或产生图形的MATLAB代码。

This approach is a combination of signal analysis and mathematical model .By the denoising function of wavelet and the learning itself function of neural network , the input and output nonlinear dynamic characteristic of system is obtained . The output prediction error, generated from the real output and wavelet neural networks estimated output, is used as a residual error to execute logical judge. and this approach can improve the speed and accuracy rate of fault detection.

它是一种把信号分析和数学模型相结合的故障检测方法,通过小波对信号的去噪和神经网络的自学习功能,来获取系统输入输出的非线性动力学特性,进而实时计算出残差并进行逻辑判决,可提高故障检测的速度和准确率。

Based on the multi-resolution analysis of wavelet, it gives a multiresolution network to combine the noise insensitiveness ability of wavelet and edge enhancement ability of the Lateral Inhibition network.

第四:将小波变换抑制噪声的功能和侧抑制网络增强边框的功能有机结合,独立提出一种基于小波变换的多分辨率分析的侧抑制网络的改进方法。

Firstly, Wavelet-Galerkin algorithm for solving the first kind of singular integral equation with the Hilbert kernel is proposed, we use the characteristic of periodic wavelet on L~2([0,1]) and Hilbert kernel to solve and make stiff matrix lower dimensions and become sparser through thresholding,thus the cost of computation is reduced. Because of the singularity of Hilbert kernel we use Tikhonov regularization method to solve the system of stiff equation. At last the convergence and numerical result of approximate solution are given. Secondly, an approach of regularization based on Fourier is presented for sideways heat equation; we give the theory proof and error estimate.

首先,提出了含Hilbert核的第一类奇异积分方程的小波伽辽金(Wavelet-Galerkin)数值算法,该算法中利用了L~2([0,1])上的周期小波和Hilbert核的特点进行处理,使得刚性矩阵维数降低并且通过阈值使得它更加稀疏,减少了计算量;由于Hilbert核的奇异性,通过Tikhonov正则化方法求解所得到的刚性方程组,给出了收敛性和数值结果;其次,对标准的一维逆热传导方程给出了一种基于Fourier正则化方法,给出了理论证明及其误差估计,解决了文献中算法与理论误差估计的不相匹配的现象,该正则化方法不仅保留了测量数据的部分高频成份,且与文献中的算法具有同样的计算量和误差估计。

Combining with discrete wavelet transform, the integral wavelet transform has been realized on hardware system, and the experimental results are satisfying.

同时结合离散整数小波变换算法,在硬件系统上实现了整数小波变换,取得了较好的试验效果。

The digital algorithms of integral and discrete wavelet transform are presented for purpose of electric power system protection, based on the analysis of wavelet transform for sampled digital signals under the Shannon sampling theorem.

从电力系统继电保护应用的角度,结合香农采样定理,深入探讨了连续信号和离散信号的小波变换的关系,给出了离散信号的积分小波变换和离散小波变换的数字计算方法。从理论上研究了信号小波变换的误差与尺度函数和小波函数的关系。

The current signals were gathered in the tests, and processed by wavelet package transform. A characteristic vector was constructed by the energy of different frequency segment, which was calculated by using the coefficient of wavelet package transform of the current signals. The vector shows that the energy of low frequency segment is of the majority of the total energy, and becomes larger as the interelectrode gap decreasing.

在线采集真实电解加工过程中的电流信号,用对实测信号进行小波包分解和重构后的小波系数计算各频段的能量,构造一个由不同频段能量组成的特征向量,发现特征向量的低频分量占总能量的大部分,随着平衡间隙的减小,低频分量能量增大。

Wavelet transform filter processing, including low-pass, high-wavelet and a number of the inverse transformation operation.

用小波变换实现滤波处理,包括低通、高通及若干层小波变换的逆变换操作。

In this paper, we firstly introduce some basic properties of wavelet, which are involved in chapter one .secondly, we introduce orthogonal wavelet bases, scale function and scale equations etc.,which are involver in chapter two .

本文首先介绍了傅里叶变换和小波变换的由来以及一些基本性质,这主要体现在第一章,随后介绍了正交小波基以及尺度函数,尺度方程等概念,这主要体现在第二章。

The simulation results show that the novel BP wavelet neural network based on IPSO not only has the good local property of wavelet analysis and the learning and classification capability of artificial neural network, but also has the fast global searching advantage of particle swarm optimization algorithm.

实验结果表明,基于改进的粒子群优化算法的BP小波网络不仅具有小波分析良好的局部特性以及神经网络的学习、分类能力,而且具有粒子群优化算法全局快速寻优的特点。

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