usermat_CDM

说明：  生成三维随机纤维的matlba程序，可以直接使用(The matlba program for generating 3D random fibers can be used directly)

说明：  MATLAB OFDM仿真代码，采用QPSK调制(MATLAB OFDM simulation code, using QPSK modulation)

fluent教程（中文帮助+UDF完全使用手册）个人由英文文档翻译，仅供参考(Fluent tutorial (Chinese help +UDF complete manual) personal English translation of the document, for reference only.)

利用BP神经网络实现对民族，古筝，摇滚，流行四类音乐的有效分类。matlab实现，提供数据和详细代码，有解释！(The BP neural network is used to realize the effective classification of four kinds of music: nationality, zither, rock and roll, and popular music. Matlab implementation, provide data and detailed code, there are explanations!)

转子系统启动时的瞬态动力学分析，有限元分析(Transient Dynamics Analysis of Rotor System at Start-up)

说明：  UPFC模型，模型非常详细和系统，有助于理解潮流控制器(UPFC model, which is very detailed and systematic, helps to understand power flow controllers)

用于温度场film边界条件，算例算例算例(This text is used for temperature field in ABAQUS user-defined subroutine which is called film.)

本文首先分析了永磁同步电机矢量控制的发展概况，然后从机电能量转换的角度出发，解释三相永磁同步电机的机电能量转换原理，推导拉格朗日运动方程。此外，列写出永磁同步电机在三相静止坐标系和dq坐标系下的数学模型。基于Simulink建立了转速电流双闭环矢量控制系统的仿真模型，通过对仿真结果分析，验证了永磁同步电机矢量控制系统性能的优越性。(This paper first analyzes the development of permanent magnet synchronous motor vector control, and then explains the electromechanical energy conversion principle of three-phase permanent magnet synchronous motor from the perspective of electromechanical energy conversion, and deduce the Lagrange motion equation. In addition, the mathematical model of permanent magnet synchronous motor (PMSM) in three phase stationary coordinate system and dq coordinate system is listed. Based on Simulink, a simulation model of speed and current double closed loop vector control system is established. By analyzing the simulation results, the performance of PMSM vector control system is verified.)

说明：  GRNN网络的预测——基于广义回归神经网络的货运量预测(Prediction of GRNN Network-- Freight Volume Forecast based on Generalized regression Neural Network)

对动态系统辨识理论做了概括性的介绍，包含了常用的非参数系统辨识方法和参数辨识方法，并将神经网络人工智能算法应用到复杂系统参数识别，适用于系统控制、参数识别与数据预测等领域的学习。传统方法与现在智能算法都配有实例，每个代码文件都有详细注释。(This paper gives a general introduction to the dynamic system identification theory, including commonly used non-parametric system identification methods and parameter identification methods, and applies neural network artificial intelligence algorithms to complex system parameter identification, which is suitable for system control, parameter identification and data prediction. Learning in other fields. Both traditional methods and current smart algorithms are equipped with examples, and each code file has detailed comments.)