C-Cmethod-for-delay-time

多因子量化选股策略，包括数据导入，分组，回测分析(Multi-factor stock selection strategies to quantify, including data import, grouping, backtesting analysis)

说明：  柴油机故障诊断SOM神经网络的数据分类MATLAB源码(SOM neural network data classification--MATLAB source code...)

小波插值与小波构造的程序，包括3个程序，有均值插值和细分插值(Wavelet interpolation and wavelet construction procedures, including three procedures, have the mean interpolation and interpolation subdivision)

multiple vector quantization watermarking

卡尔曼滤波器实现的视频目标跟踪程序，可以直接实现，对于理解该滤波器有重要作用……(Kalman filter video target tracking procedures, can be directly implemented an important role in understanding the filter ...)

支持向量机（SVM）在matlab中实现的源程序，有注释(SVM in matlab)

涉及D2D通信领域中，尤其是一种D2D通信中分布式功率控制方法。本发明解决其技术问题所采用的技术方案是：一种D2D通信中基于能效优化的分布式功率控制方法，所述控制方法包括以下步骤。本发明的有益效果主要表现在: (Involving the D2D communication field, particularly a D2D communication in distributed power control method. The invention to solve the technical scheme is adopted by the technical problems: a D2D communication in distributed power control method based on energy efficiency optimization, the control method includes the following steps. Beneficial effects of the present invention mainly displays in:)

Matlab环境下，进行二维空间波动方程的模拟。吸收边界条件(Matlab environment, the wave equation for two-dimensional simulation. Absorbing boundary conditions)

雷达系统设计MATLAB仿真一书里的第四张所有的程序，对于学习非常有用。(Radar system design MATLAB simulation fourth book program, very useful for learning.)

支持向量机和BP神经网络虽然都可以用来做非线性回归，但它们所基于的理论基础不同，回归的机理也不相同。支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。为了验证这种观点，本文编写了支持向量机非线性回归的通用Matlab程序和基于神经网络工具箱的BP神经网络仿真模块，仿真结果证实，支持向量机做非线性回归不仅泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。 (Support Vector Machine and BP neural network, even though there can be used to make non-linear regression, but they are based on the theoretical basis for the different, the mechanism of regression is not the same. Support vector machine based on structural risk minimization theory, generally considered the generalization ability of neural networks than the strong. To test this view, the paper prepared by non-linear regression support vector machine procedures and based on a common Matlab neural network toolbox of BP neural network )