taylor-function

包括IHS遥感图像融合算法，比较全。基于MATLAB。(IHS image merging)

基于MATLAB实现的来源球运动，良好的Mattlab GUI编程例程,可以直接使用。 (Realized by MATLAB source of ball movement, the good Mattlab GUI programming routines that can be used directly.)

采用db4小波对是与信号进行降噪处理，实际效果良好(Using DB4 wavelet denoising is processing and signal, the actual effect is good)

temportort meeting jakepl

　　编写一通用的预测法分析程序，要求有一定的错误处理能力，出错后能够使程序继续运行下去，直到分析过程结束。可通过不同的文法（通过数据表现）进行测试。(Write a generic prediction analysis procedure that requires a certain degree of error-handling capabilities, enabling the program to continue after an error to run it until the end of the analysis process. Can be tested by different grammar (by performance data).)

输入二项式的每一项系数和指数，输入未知函数的值求值，然后输入第二个二项式，并合并两个二项式(Enter the binomial coefficient and index of each one, enter the value of an unknown function evaluation, and then enter the second binomial, and the merger of the two binomial)

机器人的路径算法（人工智能§自动规划§用于模拟机器人规划的模拟软件 [zhangzhiguang2.rar] - 用势场法的原理实现的vc搜索最短路径程序，改进后的势场法，实用。 (机器人的路径算法（)

此程序在matlab环境下运行，是完整的tdpsola和fdppsola程序，包含了前期的语音读入，预处理，清浊音判决等等很全面 (This program runs in Matlab environment, complete the tdpsola, and fdppsola program, including pre-speech read into, pretreatment UV decision is very comprehensive)

公司专用的抄表册同步工具，仿360页面制作，配合兰德或欧珩杰抄表机使用(Copy list synchronization tool company specific, imitation 360 page production, with Rand or European Hengjie meter reading machine use )

这篇文章是盲信号处理，因为是英文文章，所以大家看内容就好了。(Principal component analysis (PCA) is a mainstay of modern data analysis- a black box that is widely used but poorly understood. The goal of this paper is to dispel the magic behind this black box. This tutorial focuses on building a solid intuition for how and why principal component analysis works furthermore, it crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA . This tutorial does not shy away from explaining the ideas informally, nor does it shy away from the mathematics. The hope is that by addressing both aspects, readers of all levels will be able to gain a better understanding of PCA as well as the when, the how and the why of applying this technique.)