MATLAB

一种基于大气退化模型的，无需原景物晴天参考图的，图像去雾算法(A fog enhance program without sunny day reference image)

模拟正弦衍射光栅光强分布，图1中X轴是位置，Y轴是光强，图2，是强度分布(Analog sinusoidal diffraction grating intensity distribution)

msk的malab仿真，其中详细绘制了msk调制解调的信号图形(msk malab the simulation, which detailed mapping msk the modulation and demodulation of signals Graphics)

吴振杨数字信号处理 实验指导书 是学习matlab不错的入门资料(DSP WUZHENYANG )

利用BP神经网络识别字母，选用改进的BP算法，精度高，抗噪声能力好(BP neural network using the letters, use the improved BP algorithm, high precision, good noise immunity)

matlab files of threshold autoregressive model STAR

利用music算法，在考虑幅度误差时，对信源个数，信噪比，快拍数进行变化，观察这三个数在什么范围变化时测向的准确度高。天线测向(Music algorithm, considering the magnitude error, the number of sources, signal-to-noise ratio, snapshots number of changes observed these three the number of measured to what extent changes to the high accuracy. Antenna direction finding)

电子节气门的控制程序，MBD模式，可仿真和生成代码，具备很好的参考价值。(Electronic throttle control procedures, MBD model, simulation, and generate code, have a good reference value.)

这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。 调用最小风险贝叶斯分类器决策子函数时，根据先验概率，再根据自行给出的5*5的决策表，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，放入决策数组中。 主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小风险贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到在一定先验概率条件下，决策表中不同决策的错误率的统计。 (This is a pattern recognition classifier minimum risk Bayes design .In reference to the case of routine , self- improvement in a certain a priori probability conditions, male , female and total error rate error rate statistics , into which each array . All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions . Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector The third step is the application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function . Bayesian classifier )

说明：  基于模糊PID的温度控制系统,通过matlab平台对基于模糊PID的温度控制系统进行设计(PID temperature controller design based on fuzzy control: fuzzy control and PID control combine to achieve PID adaptive approach to control system temperature by means of fuzzy control. This Fuzzy- PID strategy is not to replace the use of fuzzy control PID control, but the traditional control methods to improve and expand, both to maintain a simple and conventional PID control system structure, easy to use, robust, and high control accuracy advantages, but also the use of fuzzy inference PID parameters to achieve the PK, IK, DK-line self-tuning, both the fuzzy control flexibility, adaptability features, compared to simply have a good control effect either)