TransformerHse

匹配滤波器在线性调频脉冲中应用举例程序。用matlab绝对可以实现。(Linear chirp matched filter application examples in the program. With matlab can definitely be achieved.)

遗传粒子群、混沌粒子群、基本粒子群三者对比，对一个判断方程，输出其收敛曲线，本人毕业设计的核心精华！(Genetic particle swarm, chaotic particle swarm, and basic particle swarm, equations for a judgment, the output curve of its convergence, the core essence of graduation design.)

风力发电中MPPT算法中Cp系数的实现方法，亲测有效(Implementation of wind power in MPPT algorithm Cp coefficient, effective pro-test)

说明：  EMD分解算法应用于管道泄漏检测的信号处理(EMD decomposition algorithm applied to signal processing of pipeline leakage detection)

MINO各种性能仿真，适用于初学者学习使用(MINO various performance simulation, suitable for beginners to learn to use)

use alamouti for communication system.

智能洗衣机的模糊控制法则及隶属函数，可载入模块中进行使用(Fuzzy control rules and membership functions of the smart washing machines, you can load the module using the)

new kernel fast Fourier transform

风机光伏的simulink建模，能够很好的仿真风机与光伏的特性，可以扩展为任意微网或者配网中的分布式发电模块(Fan PV simulink modeling, simulation can be a good fan and photovoltaic properties, can be extended to any micro-network or distribution network of distributed generation module)

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)