kelaiman

Kernel principal component analysis (kernel PCA) [1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a non-linear mapping.

斯坦福大学机器学习课程的一个源代码例子，对于机器学习和deep learning的学习研究很有帮助(this codes are from standford university,which are about machine learning and deep learning.they will be helpful in this research field)

matlab code of wireless comunication

Self devrloped Demos for beginners of Matlab generate HTML report and enjoy

说明：  针对小流域设计洪水计算中，采用推理公式求解洪峰流量Qm及汇流时间τ的方法繁琐，查算精度较低等问题，提出用牛顿迭代法求解，该方法较原图解试算法收敛快，迭代次数少，计算速度快，精度可靠。计算实例表明，用简单迭代法与牛顿迭代法计算设计洪峰流量，其结果与图解试算法完全一致。()

this is static var compensator based on fuzzy logic controller

利用过绿色作为分类方法，分成两类后，应用马尔科夫随机场进行分割。(Made use of green as the classification method, after divided into two categories, the application of Markov random segmentation.)

利用matlab产生dll的方法、步骤。可以利用matlab来生成dll(Step method, produced by DLL matlab. You can use matlab to generate DLl)

实现立体空间的多目标空间定位，对于被动测距问题，有着深入的思考和功能仿真。(Multi-dimensional space to achieve target location, for passive ranging issues, with in-depth thinking and functional simulation.)

介绍一种有关卫星通信信号的快速盲检测方法(satellite communication signals quick blind detection)