CEC09 final codesZhao

mathlab code of ofdm based system

用自回归方法模拟脉动风载荷，MATLAB编程的实现(Realization the autoregressive method simulation of fluctuating wind load, MATLAB programming)

说明：  对于给定的信号数据，用emd进行信号分解得到固有模态函数imf(implement emd to obtain imfs)

简单的全球定位系统源码 matlab实现 easy(Simple global positioning system to achieve easy source matlab)

对雷达帧扫秒到的运动目标进行跟踪，并在MATLAB中进行仿真和验证(Frame on the radar sweep seconds to track moving objects, and carried out in the MATLAB simulation and verification)

vc与matlab混合编程的具体实例。vc调用matlab的.m函数，经过编译之后转换为C++程序，实现进行混合编程。(vc mixed with Matlab programming specific examples. Vc call Matlab. M function, compiled after the conversion of C procedures, mixed programming.)

重要参数的提取，利用matlab GUI实现的串口编程例子，gmcalab 快速广义的形态分量分析，感应双馈发电机系统的仿真，BP神经网络用于函数拟合与模式识别，抑制载波型差分相位调制，模式识别中的bayes判别分析算法。( Extract important parameters, Use serial programming examples matlab GUI implementation, gmcalab fast generalized form component analysis, Simulation of doubly fed induction generator system, BP neural network function fitting and pattern recognition, Suppressed carrier type differential phase modulation, Pattern Recognition bayes discriminant analysis algorithm.)

经典的多核学习工具箱，功能强大，内容全面(The classic multi-core learning toolbox, powerful, comprehensive)

BP神经网络容易陷于局部极小值，PSO算法在无约束非线性函数优化方面性能优越，通常可以直接找寻到全局最优解，即使不能搜多到全局最优解，也距离全局最优点不远。当然，基本PSO算法陷入局部极值也是有的。对于这个缺点目前还没有找到比较有效、省市的解决方案。本案例实现利用PSO算法和BP算法共同训练神经网络，先将网络进行PSO算法训练，然后BP算法接着进行小范围精细搜索，PSO算法训练神经网络的本质就是将输出误差函数（即能量函数）看成目标函数，PSO对能量函数进行全局寻找最小值。(BP neural networks are prone to local minimum values. The PS algorithm has superior performance in the optimization of unconstrained nonlinear functions. It can usually find the global optimal solution directly. Even if it can not find more global optimal solutions, it is not far from the global best. Of course, there is also a local extreme of the basic PO algorithm. For this shortcoming, there is no more effective, provincial and municipal solution. This case realizes the use of the SO algorithm and the BP algorithm to train the neural network. First, the network is trained in the SO algorithm, and then the BP algorithm is followed by a small range of fine searches. The essence of the PO algorithm training neural network is to regard the output error function(ie, the energy function) as the objective function, and the PO seeks a global minimum value for the energy function.)

Explores the anti-windup capabilities supported by the PID Controller block in Simulink.