CG

对于移动通信系统主要有两类衰落：大规模衰落和小规模衰落。大规模衰落表示由于大范围内移动而引起的平均信号能量的减少或路径损耗。而小规模衰落则表现为两种机制：信号的时延扩展和信道的时变特性。对于无线应用，信道的时变特性是由于发射机和接收机相对移动而造成传输路径的变化。这些传播条件的变化速率影响衰落率。如果存在大量反射路径而不存在视行信号分量，此时的小规模衰落称为瑞利衰落(Rayleigh fading)，接收信号的包络由瑞利概率分布函数统计描述。这种衰落也就是这次实验我准备研究的(system for mobile communications fading there are two main categories : large-scale and small-scale fading fading. In view of large-scale decline of large scale movement of the average energy of the signal path to reduce or loss. And the decline of small-scale, it was manifested as two mechanisms : signal delay expansion and channel time-varying characteristics. For wireless applications, channel time-varying characteristics as transmitter and receiver relative movement caused transmission path changes. These conditions spread of the rate changes affect the rate of decline. If there is a large number of pathways exist without depending on the signaling component, At that time, the decline of small-scale known as Rayleigh fading (Rayleigh fading). The received signal envelope from Rayleigh )

基于PCA主成分分析的混沌时间序列嵌入维求解算法(Based on PCA principal component analysis of chaotic time series embedding dimension algorithm)

有关自适应模糊控制的作业代码 注意要在matlab2009a以上的版本进行调试 相关使用在附件中有详细说明(The adaptive fuzzy control operation codeNote that in the above version MATLAB2009A debuggingRelated to the use of a detailed description in the annex)

Least_Square method of machine learning

The Open Porous Medium (OPM) consortium provides industry-compatible open-source finite-element flow simulation code with robust

神经网络模式识别及其实现源代码，包括 1 第3、4、6、8、9、10章代码 2 详细说明(Neural network pattern recognition and realization of the source code, including 1 3,4,6,8,9,10 Code Chapter 2 details)

ICALAB for Signal Processing Toolbox for BSS, ICA, cICA, ICA-R, SCA and MCA

一个用logistic模型编写的细胞生长曲线的模拟程序。(Prepared using a logistic model for cell growth curve of simulation program.)

X射线检测中黑度与透照参数分布曲线的最小二乘拟合(X-ray inspection of the least-squares fit blackness and Radiographic parameters distribution curve)

支持向量机是用于分类与回归的技术。由于其自身的诸多缺点，如无法获得概率输出，需要估计一个误 差参数C，以及必须使用Mercer 核函数等。相关向量机算法，克服了SVM 上述缺点，RVM 能获得与SVM 相比拟的推 广性能，并且更为稀疏。在此基础上，文中介绍了一种RVM 回归用于分类的新分类方法，用RVRC 来表示。并通过 实验证明了它的可行性。(Support vector machines for classification and regression techniques. Due to its many shortcomings, such as the probability that the output can not be obtained, need to estimate an error parameter C, and the need to use Mercer kernel function and so on. Relevance vector machine algorithm to overcome these shortcomings of SVM, RVM and SVM can be obtained comparable to promote performance and more sparse. On this basis, the paper introduces a RVM regression was used to classify new classification method, RVRC to represent. And through experiments proved its feasibility.)