Code-nonlinear-equa

在ORL或Yale标准人脸数据库上完成模式识别任务。用PCA与基于核的PCA（KPCA）方法完成人脸图像的重构与识别试验. (Or Yale in the ORL face database, complete the standard pattern recognition tasks. With the PCA and kernel-based PCA (KPCA) method to complete the reconstruction of face image and recognition test.)

dc motor project using matlab

该程序经过实际使用，是综合网上各种求小波包能量方法的正确版本，能够有效使初学者少走弯路，可直接使用，对于函数已进行了备注。(After the actual use of the program is a comprehensive online variety seeking wavelet packet energy method the correct version, so that beginners can effectively avoid detours, can be used directly for the function has been carried out notes.)

用c程序和matlab分别试验一种k-means改进算法，按照方法选取聚类中心点，事实证明，这种改进是有效的。(Matlab with c procedures and were experimenting with an improved k-means algorithm, in accordance with the method of selecting cluster center, the facts show that this improvement is effective.)

基于改进的粒子群算法的一个网络节点定位算法，并和传统的DVhop方法做了比较。(Based on the improved particle swarm algorithm of a network node localization algorithm, and the method and traditional DVhop were compared.)

matlab的一些图像处理的例子和讲解，适合习惯于用matlab画图的人使用(something about matlab)

digital signal processing, wiley publishers

书中全面系统地介绍了优化方法的基础理论和优化工具箱v2.2函数的功能、语法和工程实际应用。 (The book introduces a comprehensive and systematic optimization approach is based on theory and optimization toolbox v2.2 of functionality, syntax and engineering applications.)

PCA和KPCA综合运用，用于故障检测与诊断，希望对你有用(Integrated use of PCA and KPCA for fault detection and diagnosis have a good monitoring results, you want to be useful)

相比DeconvNet写得比较简洁易懂。但是原来代码里面给的数据似乎没法跑。所幸同作者还有一些代码用到了TCNN，比如action recognition，可以一起下载下来参考。这个代码主要特色就是tiled结构，可以用来参考，然后把里面ICA的优化函数换成RICA，Sparse Coding等等。总之，在这个代码里也了解了不少(比如line search等)。(DeconvNet well written and easy to understand compared to relatively simple. But the original code to the data inside can not seem to run. Fortunately, there are some code with the author uses TCNN, such action recognition, can be downloaded along down the reference. This code is the main feature tiled structure that can be used for reference, and then put inside the ICA optimization functions into RICA, Sparse Coding and so on. In short, this code can also learn a lot (such as line search, etc.).)