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说明：  元胞自动机源代码 用于动态仿真交通流模型 分析流量 密度 评价速度等指标(Cellular Automata source code for the dynamic simulation model to analyze traffic flow density assessment rate and other indicators)

一个duffing模型，一个噪声模型，一篇duffing检测原理，。可检测不同频率的待测信号(a duffing model, a noise model, a duffing detection principle)

数值分析, 同济大学教材<<现代数值数学和计算>>数值分析课程所有算法的matlab代码,所有例程均经过测试,私家珍藏(Numerical Analysis, Tongji University teaching <<modern numerical mathematics and computing>> numerical analysis courses all algorithm matlab code, all the routines have been tested, private collection)

确定信号和非确定信号的产生，各种滤波器的设计，对信号的频谱分析，滤波后信号的频谱分析等功能。有利于信号与系统、数字信号处理相关课程的学习与理解(Determine the signal and non-deterministic signal generation, a variety of filter design, spectral analysis of the signal, filtered signal spectrum analysis functions. Favorable signals and systems, digital signal processing related courses of study and understanding)

The model used for creating the reference voltage is shown in Fig. 4. First, photovoltaic output current (Ipv) and output voltage (Vpv) are passed through a first order low pass filter with a magnitude of G = 1 and a time constant of T = 0.01 seconds in order to filter out the high frequency components or harmonics from these signals as shown in Fig. 5 and Fig. 6. The filtered current and voltage signals (Ipv_F and Vpv_F) are then fed into the MPPT control block that uses the Incremental Conductance Tracking Algorithm. An algorithm that is based on the fact the slope of the PV array power curve shown in Fig. 7 is zero at the Maximum Power Point (MPP), positive on the left of the MPP, and negative on the right. The MPP can thus be tracked by comparing the instantaneous conductance (I/V) to the incremental conductance (∆ I/∆ V) [11] as in (1):

软硬阈值函数和自定义的基于非线性变换阈值函数曲线图(Hard and soft threshold function and custom thresholding function based on nonlinear transformation curve)

图像处理matlab 使用matlab语言对图像进行边界提取(Edge extraction image processing matlab)

matlab写的实现不同信号之间采样，卷积，重构等。里面有一个.exe是用matlab写的界面，可以打开看到效果。(matlab written between the different signals sampling, convolution and reconstruction. There is one. Exe matlab write interface, you can open to see the effect.)

利用SVM做回归线性测试。对于大盘指数的有效预测可以从整体上观测股市的变化提供强有力的信息，所以对上证指数的预测很有意义。通过对上证指数从1990.12.20-2009.08.19每日的开盘数进行回归分析，最终拟合的结果是：均方误差MSE=2.35705 e-005，平方相关系数 R=99.9195 。SVM的拟合结果还是比较理想的。(Make use of SVM linear regression testing. For the market index can predict the observed changes in the stock market as a whole to provide a strong information, so it makes sense to predict the Shanghai index. Through the Shanghai Composite Index opened daily from 1990.12.20-2009.08.19 number of regression analysis, the results of the final fit are: mean square error MSE = 2.35705 e-005, the square of the correlation coefficient R = 99.9195 . SVM fitting result is quite satisfactory.)