BICOHERX

Efficient frontier from Yahoo or database data. (Efficient frontier from Yahoo or database data.)

Matlab教训，PPT格式，从入门到精通。(Matlab lessons, PPT format, from entry to the master.)

经典PID控制教程 基于SIMULIN的源程序代码，希望对各位有帮助(pid contral tutorial,based on simulink source code)

Matlab tool for S,Z,H,Y parameter transfer(Matlab tool for S, Z, H, Y parameter transfer)

这是FCBF的matlab程序。FCBF是比较实用的特征选择和降维的方法。在这个文件里面，有个word文件详细说明了FCBF的使用方法。本站只有FCBF的java程序。(This is a matlab program of FCBF. FCBF is a more practical method of feature selection and dimensionality reduction. Within the file, a word file detailed deccribed the usage of FCBF. However, the home station only has a java program.)

支持向量机的代码。可以仿真支持向量机的效果图(support vector machine code. Simulation can support vector machines map results)

一个KNN的matlab程序，供感兴趣的人士学习(A KNN matlab program for those interested in learning)

It contains matlab codes for spectrum sensing for various channel

BP神经网络：是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hide layer)和输出层(output layer)。(BP neural network: a Press the front back propagation algorithm to train multilayer feedforward network is one of the most widely used neural network model. BP network can learn and store large amounts of input- output mode mappings without prior reveal mathematical equations that describe this map. It' s learning rule is to use the steepest descent method, through a reverse spread to constantly adjust the network weights and thresholds, so that the minimum squared error of the network. BP neural network topology, including the input layer (input), hidden layer (hide layer) and output layer (output layer).)

说明：  分变模态分解（Variational Mode Decomposition，VMD）是一种具有坚实数学基础的分解算法，能够通过建立约束变分模型表达式，将信号以非递归的方式自适应地分解为若干本征模态分量(Variational Mode Decomposition is a decomposition algorithm with solid mathematical foundation. It can adaptively decompose signals into eigenmodes in a non-recursive manner by establishing constrained variational model expressions.)