particle-swarm-algorithm

simulation power quallity in matlab simunik

2fsk调制信号在matlab里的解调实现(2fsk modulation signal demodulation in matlab to achieve)

用于观察脑电信号全通道数据和多个单通道数据，并且输出相对颅内压参数。函数通过读取nt后缀脑电图文件，并生成GUI界面，可选择全通道观察所选文件脑电信号，也可以选择包含原始EEG脑电信号，40hzEEG，80hz肌电干扰和提取出的40hzEEG信号的多个单通道观察，并且生成颅内压参数。(Used to observe all channels of EEG data and multiple-channel data, and output parameters relative intracranial pressure. 40hzEEG signal function by reading nt suffix EEG file and generates a GUI interface, you can all channels of EEG observe the selected file, or you can choose to include original EEG EEG, 40hzEEG, 80hz EMG interference and extracted a plurality of single-channel observation, and generates an intracranial pressure parameters.)

matlab codes referring to the video segmentation

说明：  关于孤子的求解和微扰对孤子的影响，特别画出了孤子的微扰的过程图(Perturbation on the soliton solution and the impact on the soliton, in particular, the soliton perturbation draw process maps)

qfsk调制，可使用各个通信系统模块，非常好用，简单易懂(qfsk modulation can be used in various communication system module, very easy to use, simple to understand)

有限差分法的Matlab程序（椭圆型方程）(Finite difference method in Matlab (elliptic equations))

Matlab code for image merging

上传的都是一些没有完成的小程序，希望大家帮帮忙，小女子这厢有礼了(I m a new student)

多变量输入、输出、多干扰、非线性和强耦合的复杂系统控制是一个比较困难的问题，常用的控制器可能因为多变量耦合问题难以控制系统。PID神经元网络是一种多层前向神经元网络，它的各层神经元个数、连接方式、连接权值是按照PID控制规律的已有原则和经验确定的，是一种动态的符合控制系统的前向网络。但是由于PID网络初始权值随机取值的原因，每次控制的效果都有所差别，个别情况下控制效果还比较差。本案例研究了基于PID神经元的多变量耦合系统控制，并用PSO算法来优化控制器以取得更好的控制效果。(Multivariable input, output, and more interference, complex nonlinear and strong coupling system control is a more difficult problem, commonly used multivariable controller may be because the problem is difficult to control the coupling system. PID neural network is a multi-front to neural networks, its number of neurons in each layer, connection, connection weights are in accordance with the existing principles and experience to determine the PID control law is a dynamic compliance before the control system to the network. However, due to the random initial weights of the network PID values ​ ​ of reason, every time there are differences in the effect of control and in some cases the control effect is still relatively poor. This case study of multivariable coupling system based on PID control neurons, and with PSO algorithm to optimize the controller to achieve better control effect.)