paper2

遗传算法工具箱，可以根据其中文件提示进行修改，还可以自己编写文件嵌入(Genetic algorithm toolbox, based on documents which prompted changes can also be embedded in their own documentation)

说明：  MATLAB神经网络工具箱理论与应用，解压后需用超星阅览器打开(MATLAB neural network toolbox theory and application, need to use SSReader open after decompression)

雷达原理中matlab实现脉冲压缩仿真的代码(it implements Pulse compression)

a image for async show

bp神经网络，用于拟合函数，数据融合方面的应用，可以通过训练网络来达到拟合所需数据的目的(bp Neural network)

常用的分形图像的生成程序，算法为确定性算法。(interpolation)

适合使用NUVOTON nuc501 做开发的参考。(The NUC501 is an ARM7TDMI-based MCU, specifically designed to offer low-cost and high-performance for various applications, like interactive toys, edutainment robots, and home appliances. It integrates the 32-bit RISC CPU with 32KB high-speed SRAM, crypo engine with OTP key, boot ROM, LDO regulator, ADC, DAC, I2C, SPI, USB2.0 FS Device, & GPIO into a cost-affordable whil feature-rich micro-controller.)

提取信号七个基于瞬时信息的特征：零中心归一化瞬时幅度功率谱密度的最大值,零中心归一化瞬时幅度绝对值的标准偏差，零中心非弱信号段瞬时相位非线性分量绝对值标准偏差，零中心非弱信号段瞬时相位非线性分量标准偏差，零中心归一化的非弱信号段瞬时频率绝对值的标准偏差，一个信号段的归一化瞬时频率功率谱密度的最大值，根据信号 QPSK 和16QAM在 XI 轴投影的不同表现，提出特征参数。(Extraction based on instantaneous information signal of seven characteristics: zero center normalized instantaneous amplitude of the maximum power spectral density, zero-center normalized standard deviation of the instantaneous rate of absolute zero center section of non-weak signal instantaneous phase nonlinear component absolute value of standard deviation, non-zero center section of the weak signal non-linear component of the standard deviation of the instantaneous phase, zero-center non-normalized absolute value of the weak signal section of the standard deviation of the instantaneous frequency of a signal segment of the normalized power spectral density of instantaneous frequency maximum, according to the signal QPSK and 16QAM in the XI-axis projection of the different performance of the proposed characteristic parameters.)

基于传递函数描述的3输入3输出DMC控制器设计代码，有详细的备注，可编译运行(DMC controller design method)

K-L+LDA结合了K-L和LDA优点的一个算法(K-L+ LDA combination of KL and the advantages of an algorithm for LDA)