plantmodel

一个FDTD二维的经典程序，非常不错，适合初学者使用。(A classic two-dimensional FDTD program, very good, suitable for beginners.)

FFt例程，具有Pdf文档的具体说明过程。不错的啊！(FFt routines, Pdf file with the specified process. Good ah!)

FIR Filter的例子，大学研究室的。(FIR filter example, the university lab.)

用Matlab的GUI界面设计滤波器人机界面，可选FIR、IIR等高通，低通，带通，带阻滤波器，手动设定好参数，可直观看到幅频特性曲线和相频特性曲线。(Using Matlab GUI interface design a filter interface, optional FIR, IIR high pass, low pass, band pass, band stop filter, the parameters are set manually, you can visually see the amplitude-frequency characteristic and phase-frequency characteristic curve .)

用MATLAB和VC对图像用SUSAN算法和Harris算法进行图像角点提取，还有自适应的SUSAN算法的角点提取(MATLAB and VC with SUSAN Harris algorithm for image image corner extraction, and adaptive SUSAN corner extraction algorithm)

Code used by Ferman (2011) in the matlab.

Partial integration with system SyncManager we tell our EAS SyncManager to start a calendar sync when we get the signal from the system SyncManager.

用改进遗传算法求解TSP问题,并编制了完整的Matlab程序予以仿真实现.程序中选择算子采用最佳个体保存与赌轮选择相结合的策略,最后分析了最佳个体保存比例对寻优效果的影响. 更多还原 (Improved genetic algorithm for TSP, and the preparation of a complete Matlab simulation program to be achieved. Program, select the operator uses the best individual preservation and roulette wheel selection strategy of combining, the final analysis of the optimal ratio of individual saving optimization results impact. more reduction)

永磁同步电动机的开环控制的matlab/simulink仿真。供大家学习。(Permanent magnet synchronous motor open-loop control matlab/simulink simulation. For everyone to learn.)

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.)