niujiu

QPSO和FDTD相结合的英文文章，费了好大劲找到的(FDTD combined QPSO and English articles, and with great enthusiasm to find)

this source code is good

现代信号处理中功率谱估计的Burg算法，程序简单直观。(the algorithm in modern signal processing.)

p1 - the first parent p2 - the second parent bounds - the bounds matrix for the solution space Ops - Options matrix for simple crossover(p1- the first parent p2- the second parent bounds - the bounds matrix for the solution space Ops - Options matrix for simple crossover)

本代码是ja-sw 矢量磁滞模型的混合模型的代码，有两部分一部分是ja模型，另一部分是sw模型，两者混合变为矢量磁滞模型(This code is ja-sw vector hysteresis model hybrid model of the code, there are two parts ja model part, the other part is sw model, a combination of both into a vector hysteresis model)

使用原模图法的LDPC进行编译码改进，并展示了改进后误码率以及信噪比的变化(Use protograph LDPC encoding and decoding method to improve and show a change in the error rate as well as the improved signal to noise ratio)

一段基于自抗扰控制器的仿真代码( )

粒子滤波的Matlab仿真算法实现  一个粒子滤波Matlab算法(Matlab simulation algorithm particle filter algorithm to achieve a particle filter Matlab)

MATLAB工具箱的神经网络理论与应用程序源代码 丛爽编，国科学技术大学出版社(Toolbox for MATLAB neural network theory and application source code is compiled Cong Shuang, State University of Science and Technology Press)

说明：  相关向量机（Relevance Vector Machine，简称RVM）是Micnacl E.Tipping于2000年提出的一种与SVM（Support Vector Machine）类似的稀疏概率模型，是一种新的监督学习方法。 它的训练是在贝叶斯框架下进行的，在先验参数的结构下基于主动相关决策理论（automatic relevance determination，简称ARD）来移除不相关的点，从而获得稀疏化的模型。在样本数据的迭代学习过程中，大部分参数的后验分布趋于零，与预测值无关，那些非零参数对应的点被称作相关向量（Relevance Vectors），体现了数据中最核心的特征。同支持向量机相比，相关向量机最大的优点就是极大地减少了核函数的计算量，并且也克服了所选核函数必须满足Mercer条件的缺点。(Relevance Vector Machine (RVM) is a sparse probability model similar to SVM (Support Vector Machine) proposed by Micnacl E. Tipping in 2000. It is a new supervised learning method. Its training is carried out under the Bayesian framework. Under the structure of prior parameters, it is based on Automatic Relevance Determination (ARD) to remove the irrelevant points, so as to obtain the sparse model. In the iterative learning process of sample data, the posterior distribution of most parameters tends to zero, which is independent of the predicted value. The points corresponding to non-zero parameters are called Relevance Vectors, which represent the most core features of the data. Compared with support vector machine, the biggest advantage of correlation vector machine is that it greatly reduces the computation amount of kernel function, and also overcomes the shortcoming that the selected kernel function must meet Mercer's condition.)