06153042

说明：  以优化SVM参数c和g为例，对FA算法MATLAB源码进行了详细中文注解，是很好的学习材料(Taking the optimization of SVM parameters c and G as examples, the source code of FA algorithm MATLAB is annotated in detail in Chinese, which is a good learning material.)

基于李雅普诺夫稳定性分析的模型参考自适应系统仿真(Based on the lyapunov stability analysis model reference adaptive system simulation based on lyapunov stability analysis model reference adaptive system simulation )

深度学习matlab工具箱,包括深度deep belief nets,stacked autoencoder,convolutional neural nets等网络。(Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.)

基于pls对光谱分析 包括数据读取，小波变换PCA分析，PLS建模，交叉验证(Pls include data on the spectrum based on reads, wavelet transform PCA analysis, PLS modeling, cross-validation)

计算序列的自相关函数,绘制信号xn的自功率谱幅频曲线,绘制信号yn的自功率谱幅频曲线,计算互谱函数(Calculating the autocorrelation function of the sequence, drawn from the power spectrum of the signal xn amplitude-frequency curve, drawn from the power spectrum of the amplitude-frequency curve signal yn calculated cross-spectral function)

遗传算法解决作业车间调度问题，,这一M文件计算目标函数值。(Genetic algorithms to solve the job shop scheduling problem,, the M-file to calculate the value of the objective function....)

a matlab code to reduce the dimenision of data. using probability principle analysis

digital signal processing

其内容有： 1.基带输入波形及其功率谱 2.QPSK调制信号及其功率谱 3.AWGN信道输出及其功率谱 4.通过瑞利信道之前和之后的信号星座图 5.在高斯白噪声和瑞利衰落信道条件下的误码性能以及高斯白噪声的理论曲线，所有误码性能曲线在同一坐标比例下绘制 (Bird 其 bleed at the nose 容 To: 1. 入 price turbulent Shu Huai Zhi形其 谱 2.QPSK Carex Kun Qin Zhi制信ulcers 其 Carex Kun 谱 3.AWGN信die HUAI embankment loved Kun 其 Carex 谱 4. 瑞 Services Toru Hitomi信die Core之前之up cavity ulcers信standard 5 star座. Now Inquiry assistant噪dumb声Rui Hitomi 瑞衰Miao Li Shu信die cavity误your tungsten loved consultation can begin Xikui NAI噪dumb声cavity理Sinopodisma 曲 All线，误bleed at the nose W Chu线曲 Xikui Now filthy instance, bird)

一个简单的PID simulink控制倒立摆的模型建立(A simple PID simulink control of inverted pendulum model building)