psat-2.1.8-mat

混沌时间序列 基于人工神经网络的一步或多步预测(Chaotic time series based on artificial neural network or multi-step prediction step)

说明：  零件参数设计：优化零件的参数设计，提高机器效率。(Part Parameter Design: Optimizing the parameters of components designed to improve the efficiency of the machine.)

先进控制技术 模型预测控制技术 动态矩阵控制算法 matlab仿真(Advanced control technology model predictive control technology dynamic matrix control algorithm matlab simulation)

该代码可以用，MATLAB实现图像信息熵计算，在图像处理中很实用，评价图像质量。(This code can be used, MATLAB for image information entropy calculations, image processing is very practical, uate the image quality.)

通过画图找出一个boxcox转换的lambda值(Find out the lambda value of a box Cox transformation by drawing)

这是按照DTMB电视标准用matlab写的生成LDPC矩阵的源代码，可以运行。(This is a code that is in accordance with the standard DTMB TV source code, written in matlab to generate LDPC matrix and it can be run.)

MATLAB的hopfied神经网络实验数据和程序(MATLAB-hopfied experimental data and procedures for neural networks)

说明：  基于TV模型的图像快速修复自适应方法，欢迎大家分享(TV model based on adaptive methods of image quick fix, please share)

功率谱估计 信号为两个正弦信号加高斯白噪声，各正弦信号的信噪比均为10dB，长度为N，信号频率分别为 和 ，初始相位 ，取 ， 取不同数值：0.3，0.25。 为采样频率。分别用Levinson递推法和Burg法进行功率谱估计，并分析改变数据长度、模型阶数对谱估计结果的影响。(Power spectrum estimation signal into two sinusoidal signals applied Gaussian white noise, the SNR of each sinusoidal signal are 10dB, a length of N, respectively, and the signal frequency, initial phase, take, take different values: 0.3,0.25. Is the sampling frequency. Levinson recursive method were used and Burg method for power spectrum estimation, and change the length of the data analysis, the impact on the estimation model order spectrum.)

设计一个阻带下限0.45*pi 阻带上限0.8*pi 通带下限0.55*pi 阻带上限0.7*pi 通带衰减0.15dB，阻带衰减40dB，分别用汉宁窗汉明窗凯瑟窗布莱克曼窗设计滤波器，对比分析各方法优劣(Design a stop-band limit 0.45* pi stopband limit 0.8* pi Passband lower limit 0.55* pi stopband limit 0.7* pi Passband attenuation 0.15dB, stop-band attenuation of 40dB, respectively Hanning Hamming window Kaiser window Blackman window filter design, comparative analysis of the pros and cons of each method)