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说明：  神经网络在轧钢张力控制中的应用。使用MATLAB编写的程序(very good)

基于蒙特卡洛仿真仿真的高斯白噪声中确知信号的检测性能曲线(Detection performance curve of Gauss white noise based on Monte Carlo simulation)

贪食蛇 游戏 matlab—gui 可以选择上下左右走 碰墙结束游戏 可以修改格子大小(sanck is a classical game by matlab )

Newton-Raphson Power Flow Algorithm

HISTNORM Histogram normalized [...] = HISTNORM(...) works like HIST, but the frequency is normalized so that area sum is 1. Bonus usage! [...] = HISTNORM(..., plot ) plots and returns the output arguments. Be sure plot is the last argument. Example: data = randn (10000, 1) [xo,no] = histnorm(data, 101, plot ) hold on plot (no, normpdf(no), r ) hold off See also: HIST.

遗传优化RBF采用遗传算法对传统RBF网络进行优化(RBF genetic optimization using genetic algorithms to optimize the traditional RBF network)

多种插值方法，可以用于改进EMD，EEMD等时频方法的插值。(A variety of interpolation methods can be used to improve EMD, EEMD and other time-frequency interpolation method.)

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.)

矩量法求半波振子天线的阻抗,与理论值偏差较小，比较精确(mom to calculate the z of the antenna)

My demonstration program is coded by MATLAB. For the program, some optimization at coding level and algorithm level are proposed to improve the performance of my fingerprint recognition system. These performance enhancements are shown by experiments conducted upon a variety of fingerprint images. Also, the experiments illustrate the key issues of fingerprint recognition that are consistent with what the available literatures say.