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Morlet小波变换源程序，开发环境为Matlab(Morlet wavelet transform code using Matlab)

说明：  MATLAB神经网络应用设计，适合初学者(MATLAB neural network application design, suitable for beginners)

说明：   该程序在matlab-simulink环境下执行，名为classpiddistb2.mdl，(经典PID的扰动测试)，本人发前期发.m格式文件(NNPIDDistub2.m为神经网络动态pid抗绕程序)，本程序为前期程序的比较程序，已发于本人论文，可供借鉴和学习。(The program matlab-simulink environment, implementation, called classpiddistb2.mdl, (classical PID disturbance test), I made pre-made. M format (NNPIDDistub2.m for the neural network dynamic pid anti-around program), this procedure pre- The Comparative procedures have been made in my paper, for reference and learning.)

说明：  基本的图像处理技术在MATLAB里得到非常好的应用(The basic image processing technology in the years to be a very good application of MATLAB)

matlab的书 不容错过，大家一起来看(matlab book not to be missed, everyone together)

deep learning一般是学习的层次结构，它也是有一定理论依据的，即模拟人脑的大脑皮层工作，因为大脑皮层的视觉区域也是分层次工作的，越底层的视觉皮层对那些底层特征就越敏感。综上所述，feature learning有这么多应用需求的驱动和生物神经理论上的支持，注定它能够在AI领域中发挥一定的作用。一些实验表明，有些feature learning学习到的特征几乎比所有其它的特征效果要好，比如本文中的ISA模型就是其中一个。(deep learning hierarchy of learning, it is also a certain theoretical basis that simulate the human brain' s cerebral cortex, visual areas of the cerebral cortex is hierarchical work, the more the bottom of the visual cortex is more sensitive to those underlying characteristics . In summary, feature learning so many application needs the support of the drive and biological neural theoretically doomed it to be able to play a certain role in the field of AI. Some experiments show that some feature learning learning to feature almost better than all the other features of the effect of the ISA model such as this is one.)

Towards Human Exploration of Space: a EUropean Strategy

matlab实现二维抽样定理的模拟，通过peak函数产生的图像，进行二维抽样后还原出原图样(MATLAB realizes the simulation of two-dimensional sampling theorem, and restores the original pattern after two-dimensional sampling through the image generated by peak function.)

a gui used for controling robot

EEGLAB的插件，该插件和EEGLAB结合可以根据独立成分的地形分布特征和时域特征去除相应的伪迹(this plug-in is used to reject artifact of EEG, which is achieved through the topographical feature and temporal feature of independent component)