Kalman

射频功率放大器的数学建模，采用saleh模型在matlab环境下进行代码编写(hebavioral model of RF amplifier)

monte carlo method implemented on matlab : presentation + demo source code on matlab

ABAQUS UMAT 带剪切非线性HASHIN准则(ABAQUS UMAT)

实现典型相关分析，均值便宜跟踪的示例，处理信号的时频分析，有小波分析的盲信号处理，现代信号处理中谱估计在matlab中的使用，最小二乘回归分析算法。( Achieve canonical correlation analysis, Example tracking mean cheap, When processing a signal frequency analysis, There Wavelet Analysis Blind Signal Processing, Modern signal processing used in the spectral estimation in matlab, Least-squares regression analysis algorithm.)

典型相关分析，分析两段数据之间的相似性，用于脑电ssvep信号分析(Canonical correlation analysis is used to analyze the similarity between two segments of data.)

此程序用于计算高光谱数据研究中的红谷吸收深度和绿峰反射高度(This program used to calculate the spectral data of the absorption depth of red and green valley high peak reflectivity)

安照标准格式给出数据信息excel文档，可以处理数据并形成可视化模型，包含原始excel文档与m文件(Security is given according to standard format data excel document, can handle data and develop visual models, including the original excel file documents and m)

一个小的程序,刚刚学习matlab的同学可以下来(A small process, the students just learning matlab can be down)

partial autocorrelation plot to decide the order of arima model

本文采用小波神经网络进行交通流量预测，短时交通流量存在随机性和非线性因素，影响预测的准确性。传统预测模型难以反映交通流量变化特点，同时传统神经网络易陷入局部极小值，泛化能力差，交通流量预测精度低。为了提高短时交通流量预测精度，提出一种小波神经网络的短时交通流量预测模型。小波神经网络可以对短时交通流量随机性、不确定性进行局部分析，并进行非线性预测，验证了模型的有效性，进行了对比试验。验证结果表明，小波神经网络提高了短时交通流量预精度，预测结果更具应用价值。(In this paper, wavelet neural network is used to forecast traffic flow. There are random and nonlinear factors in short-term traffic flow, affecting the accuracy of prediction. The traditional prediction model is difficult to reflect the characteristics of traffic flow changes. At the same time, the traditional neural network is easy to fall into the local minimum, the generalization ability is poor, and the prediction accuracy of traffic flow is low. In order to improve the accuracy of short-term traffic flow forecasting, a short-term traffic flow prediction model based on wavelet neural network is proposed. The wavelet neural network can analyze the randomness and uncertainty of short-term traffic flow and perform nonlinear prediction. The validity of the model is verified and a comparative experiment is conducted. The verification results show that the wavelet neural network improves the short-term traffic flow pre-precision, and the prediction result has more application value.)