MakeyourContents

基于T-S模型的预测控制。为Matlab文件，附有模型说明。 (TS model based predictive control. For the Matlab files, with the model description.)

这是一本非常适合初学者学习matlab的书，讲述很详细，易懂，希望对大家有帮助。(This is a great book for beginners to learn matlab, tells a very detailed, easy to understand, we want to help.)

snake方法的主要局限性在于要求初始化要靠近期望的解和对边界的凹形部分的分割存在困难，GVF解决了这两个问题，并将其用于snake图像分割(The main limitation is the snake method requires initialization to be close to the desired solution and the boundary of the concave part of the division of the difficulty, GVF solve these two problems, and for the snake segmentation)

多机器人队形初始化，使用11个机器人最终编队形成一个圆形(Multi-robot formation initialization)

用于改进的FSC算法，求取特征基因，很好的算法。(FSC improved algorithm for obtaining genetic characteristics, good algorithm.)

面是我写的 NSCT-PCNN 图像融合程序，论文见： 屈小波, 闫敬文, 肖弘智, 朱自谦.非降采样Contourlet域内空间频率激励的PCNN图像融合算法.自动化学报，Vol.34, No.12, pp: 1508-1514.Dec.2008. Matlab Image Fu...

几乎涵盖了光学各种仿真的学位论文，值得大家去深入学习。matlab的使用会进一步加深。(Almost covers the optical simulation of the dissertation, worth to study further. The use of matlab will further deepen.)

These files contain all the code necessary to run the example in the Webinar "Signal Processing for Machine Learning in MATLAB". They also include code to automate the download and preparation of the dataset used. In that webinar we presented an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We used consolidated signal processing methods to extract a fairly small number of highly-descriptive features, and finally trained a small Neural Network to map the feature vectors into the 6 different activity classes of a pre-recorded dataset. The topics discussed include: * Signal manipulation and visualisation * Design and application of digital filters * Frequency-domain analysis * Automatic peak detection * Feature extraction from signals * Train and test of simple Neural Networks

I simulate a very simple interfrogram

泊松网络 平均节点数是5的1000个节点的泊松网络结构 均匀分布的网络(Poisson network the average number of nodes is 5 Poisson 1000 nodes uniformly distributed network architecture network)