sweep

这是一本有关 MATLAB 的参考书，适合于使用 MATLAB 或正在打算使用MATLAB 的读者。本书另辟蹊径可以借助或不借助 MATLAB 随带的文档资料让读者自学 MATLAB。书中口语化的风格,使读者易于阅读。(This is a reference book about MATLAB, suitable for use or are planning to use MATLAB MATLAB readers. This book can open a new path with or without the help of MATLAB documentation that comes with the reader learning MATLAB. Colloquial style of the book, the reader easy to read.)

fastica 工具包，独立成分分析。安装在matlab的toolbox下即可使用(fastica toolbox，for ica analysis)

matlab的regionpros函数的具体用法及相关例程(matlab' s regionpros function of specific usage and related routines)

这是第二能量熵的matlab代码，wolf 方法计算李雅普诺夫指数,正确率可以达到98％。( This is the second energy entropy matlab code, wolf calculated Lyapunov exponent, Accuracy can reach 98 .)

matlab training power point

matlab基础教程，适合初学者学习matlab的基本功能(Matlab based tutorial, suitable for beginners to learn the basic function of matlab)

一种雷达系统建模与matlab仿真，具体介绍了MATLAB的仿真(Modeling and Simulation of radar system )

说明：  16QAM 仿真程序 有星座图 简单实用的程序(16QAM constellation simulation procedure is simple and practical procedure)

ep. // Modules communicating with external circuits should normally be disabled // before entering a sleep mode that will stop the module operation. // Make sure the USART dumps the last message completely before turning it off. while(!usart_tx_empty(EXAMPLE_USART)) pcl_disable_module(EXAMPLE_USART_CLOCK_MASK)

这是用身高体重数据进行性别分类的实验。 用最小错误率贝叶斯分类器决策时，首先通过比较概率大小判断一个体重身高二维向量代表的人是男是女，然后再逐一与已知性别的数据比较，就可以得到错误率的统计。然后改变先验概率，重复上面的过程，观察数据结果的变化。 用最小风险贝叶斯分类器决策时，首先求出用最小错误率贝叶斯分类器得到的条件概率；然后根据人为给定的决策表，根据公式算出条件风险；然后逐一比较条件风险，找出使条件风险最小的决策（也就是分类）。最后用分类得到的结果逐一比较已经知道的原始数据，统计处错误率。 (This is the height and weight data for gender classification experiment. With the minimum error rate Bayesian classifier decisions , first by comparing the probability of the size and weight to height to determine a person represented by two-dimensional vector is male or female , and then one by one with known gender data comparison, the statistical error rate can be . Then change the prior probability , repeat the above process , the results of the changes observed data . Bayesian classifier with the minimum risk decision-making , first find the minimum error rate using Bayesian classifier to get the conditional probability then artificially given decision table , according to the formula to calculate conditional risk and then one by one more conditional risk , to find ambassador to the conditions of minimum risk decision making (ie classification) . Finally, the results obtained with the classification by-side comparison of the raw data have been aware of SD error rate . )