BPNN-Matlab

《通信系统仿真原理与无线应用》系统地介绍通信系统的设计、分析和仿真方法，还提供了大量极具参考价值的仿真实例。《通信系统仿真原理与无线应用》的全部仿真程序都是采用MATLAB编写的。这不仅方便读者理解仿真的实现过程，还允许读者修改或使用这些代码段来进行自己的仿真开发。章后提供了大量的参考文献和经过精心设计的各类练习题，方便教学。 此为英文原版书籍(Principles of Communication Systems Simulation with Wireless Applications is a hands-on, example-rich guide to simulating wireless communications systems. The first book to present complete MATLAB simulation models for predicting the impact of design changes, it treats every aspect of simulation: sampling, signal and system representations, filters, noise, Monte Carlo simulation, postprocessing, nonlinear and time-varying systems, waveform and discrete channels, co-channel interference, and more. It includes four detailed case studies.)

对非同步采样数据进行二次插值同步化处理，进而进行谐波分析。(The asynchronized sample data can be synchronized by the qudric interpolation.)

基于最小二乘支持向量机理论,结合某风电场实测风速数据,建立了最小二乘支持向量机风速预测模型。对该风电场的风速进行了提前1h的预测,其预测的平均绝对百分比误差仅为8.55 ,预测效果比较理想。同时将文中的风速预测模型与神经网络理论、支持向量机(support vector machine,SVM)理论建立的风速预测模型进行了比较。仿真结果表明,文中所提模型在预测精度和运算速度上皆优于其他模型。 (Based on least squares support vector machine theory, combined with a wind farm measured wind speed data, the establishment of a wind vector machine forecasting model of least squares support. The velocity of the wind farm were predicted in advance 1h, the mean absolute percentage error of only 8.55 of its forecast, forecast effect is ideal. While the text of the wind speed forecasting models and neural networks, support vector machines (support vector machine, SVM) wind speed prediction models were compared with established theories. Simulation results show that our proposed model on prediction accuracy and computing speed are superior to other models.)

这是一个关于稀疏高斯过程的matlab源码，可以用于计算测试输入的高斯预测值。( spgp_pred computes the SPGP predictive distribution for a set of test inputs. You need to supply a set of pseudo-inputs or basis vectors for the approximation, and suitable hyperparameters for the covariance. You can use any method you like for finding the pseudo-inputs , with the simplest obviously being a random subset of the data. It is coded for Gaussian covariance function, but you could very easily alter this. It is also fine to use for high dimensional data sets. spgp_lik is the SPGP (negative) marginal likelihood and gradients with respect to pseudo-inputs and hyperparameters. So you can use this if you wish to try to optimize the positioning of pseudo-inputs and find good hyperparameters, before using spgp_pred . I would recommend initializing the pseudo-inputs on a random subset of the data, and initializing the hyperparameters sensibly. Its current limitations are that 1) it is slow and memory intensive for high dimensional data sets 2) it is heavi)

dynamic signal analyzer

code of implementing the K-Nearest Neighbor method

时变copula的matlab程序，正态copula，t-copula，clayton copula，sjc copula(dynamic copula toolbox)

实现FIR滤波器设计，执行代码可以进行FIR滤波功能。(FIR frighting desgion.)

pack unpacked bcd nimbers

cdma simulation using mat lab code