DSP1_22

this code shows the new filter application ........................................

matlab用于进行母线负荷预测，选择K作为样本的时间长度，T为连续预测的时间。数据格式为2个表格，一个是负荷数据一个是日期。(matlab used for bus load forecasting, K as the length of the sample, T is continuous forecast time. The data format is two forms, one is a load of data is a date.)

利用区域生长法在图像中提取出所需要的部分，本程序为车道线(Using region growing method to extract the required image portion, the procedures for the Drive Line)

说明：  MIMO相关的空时编码 包括调制编码等等(MIMO related to the modulation coding space-time coding, etc.)

实现第一类hankel函数及其导数的一致渐进展开H_v^{(1)}(k_z)(Achieve consistent first class hankel function and its derivatives, asymptotic expansions H_v ^ {(1)} (k_z))

说明：  matlab编写的潮流程序，内附30个节点的验证(matlab program written in the tide, containing 30 nodes verify)

matlab平台下的一种遗传算法，可用于波段选择或者波段选择(A Genetic Algorithm-Based Feature Selection)

模型辨识 模糊神经网络 T-S模型 输入输出 前提参数 结论参数(Model Identification of TS Fuzzy Neural Network model input and output parameters of the premise parameters Conclusion)

This tutorial is concerned with applications of information theory concepts in statistics. It originated as lectures given by Imre Csisz´ ar at the University of Maryland in 1991 with later additions and corrections by Csisz´ ar and Paul Shields.

In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.