decision-tree

用C语言编写的在Simulink环境下对转速可调永磁直流电机进行数值仿真的S函数源代码。该仿真源代码的特点是：能够仿真目前广泛应用的H桥驱动PWM调制调速永磁直流电机。将功率器件H桥与电机本体的数学模型完整的结合到一起。执行效率很高，仿真结果与实验结果非常相近。(C language in the Simulink environment for adjustable speed permanent magnet DC motor simulation S-function source code. The simulation source code features : simulation can be widely applied to the H-bridge PWM drive permanent magnet DC motor speed control. Will power devices H-bridge and motor Ontology integrity of a mathematical model combining together. High efficiency in the implementation of simulation results and experimental results are very similar.)

AN MATLAB DESIGN OF PV ARRAY for zsource

本程序描述了地球，月亮以及卫星三者之间的关系，参数可以设置。(This procedure describes the Earth, the Moon and the relationship between the three satellites, the parameters can be set.)

物理层的空时编码的MATLAB仿真程序，包含发射端、信道到接收端的所有源代码(space-time coding project)

说明：  example for linear optimization

捷联惯导解算程序四元数法，对飞机航姿系统的简单化很重要。(SINS solver quaternion method, the simplicity of the aircraft AHRS is very important)

给定一个控制系统，利用单纯形法整定PID参数,matlab程序(setting PID parameters with simplex method )

说明：  基于稀疏表示的算法，对Donoho提出的Cartoon image进行纹理综合(Algorithm based on sparse representation of Donoho proposed Cartoon image texture synthesis)

This document is useful for hearing sound classification

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)