C++完美演繹 經(jīng)典算法 如 /* 頭文件：my_Include.h */ #include <stdio.h> /* 展開C語言的內(nèi)建函數(shù)指令 */ #define PI 3.1415926 /* 宏常量，在稍后章節(jié)再詳解 */ #define circle(radius) (PI*radius*radius) /* 宏函數(shù)，圓的面積 */ /* 將比較數(shù)值大小的函數(shù)寫在自編include文件內(nèi) */ int show_big_or_small (int a,int b,int c) { int tmp if (a>b) { tmp = a a = b b = tmp } if (b>c) { tmp = b b = c c = tmp } if (a>b) { tmp = a a = b b = tmp } printf("由小至大排序之后的結(jié)果:%d %d %d\n", a, b, c) } 程序執(zhí)行結(jié)果： 由小至大排序之后的結(jié)果:1 2 3 可將內(nèi)建函數(shù)的include文件展開在自編的include文件中 圓圈的面積是=201.0619264
This program demonstrates some function approximation capabilities of a Radial Basis Function Network.
The user supplies a set of training points which represent some "sample" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the variance for the network. Using the training samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to approximate the function between the given "sample" points.
A fast customizable function for locating and measuring the peaks in noisy time-series signals. Adjustable parameters allow discrimination of "real" signal peaks from noise and background. Determines the position, height, and width of each peak by least-squares curve-fitting.
New users and old of optimization in MATLAB will find useful tips and tricks in this document, as well as examples one can use as templates for their own problems.
Use this tool by editing the file optimtips.m, then execute blocks of code in cell mode from the editor, or best, publish the file to HTML. Copy and paste also works of course.
Some readers may find this tool valuable if only for the function pleas - a partitioned least squares solver based on lsqnonlin.
This is a work in progress, as I fully expect to add new topics as I think of them or as suggestions are made. Suggestions for topics I ve missed are welcome, as are corrections of my probable numerous errors. The topics currently covered are listed below
We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation,
phase-shift keying, and pulse amplitude modulation
communications systems.We study the performance of a standard
CFO estimate, which consists of first raising the received signal to
the Mth power, where M is an integer depending on the type and
size of the symbol constellation, and then applying the nonlinear
least squares (NLLS) estimation approach. At low signal-to noise
ratio (SNR), the NLLS method fails to provide an accurate CFO
estimate because of the presence of outliers. In this letter, we derive
an approximate closed-form expression for the outlier probability.
This enables us to predict the mean-square error (MSE) on CFO
estimation for all SNR values. For a given SNR, the new results
also give insight into the minimum number of samples required in
the CFO estimation procedure, in order to ensure that the MSE
on estimation is not significantly affected by the outliers.
