Circul-plot

this mfile is riten in matlab and is related to mirtoolbox

来源于国外论文，可以生成pv曲线，亲测有用(Papers abroad, you can generate pv curve, pro-test useful)

说明：  都是关于空间普估计算法，其中有解相干和非相干算法以及它们之间的对比( the spatial spectrum estimation)

基于杂交的粒子群优化函数--借鉴遗传算法的杂交概念，在每次迭代中，根据杂交概率选取指定数量的例子放入杂交池中，里面的例子随机两两杂交，产生相同数目的子代例子(Hybridization-based particle swarm optimization functions- Reference hybrid genetic algorithm concepts, in each iteration, according to the selected crossover probability into a specified number of examples of hybrid pool, examples of which twenty-two random hybridization, the same number of offspring produced example)

多縫夫朗禾費衍射模擬,對於仿真干涉模擬有非常大的幫助(Multi-slit Fraunhofer diffraction Wo fees simulation, emulation interference simulation for a very big help)

我写的一个地震单台定位的程序，可以输入初至和P波和S波的到时差就可以计算出震中，然后输入台站的经纬度就可以得到震中的经纬度(I wrote a single earthquake location procedures, may enter the beginning to and P-wave and S wave to the time difference can be calculated on the epicenter, and then enter the station s latitude and longitude can be the epicenter of the latitude and longitude)

format changer - hex to decimal - decimal to hex

说明：  《模糊ART和模糊ARTMAP 神经网络》这些文件包允许你创建、训练、测试模糊ART和模糊ARTMAP神经网络。 作者: Aaron Garre("vague and ambiguous ARTMAP ART neural network" These packages allow you to create, training, testing and Fuzzy Fuzzy ART ARTMAP neural networks. Author : Aaron Garre)

清空环境变量 网络结构建立 遗传算法参数初始化 迭代求解最佳初始阀值和权值 遗传算法结果分析 把最优初始阀值权值赋予网络预测 BP网络训练 BP网络预测 清空环境变量(Empty the environment variable Established network structure Genetic algorithm parameter initialization Iterative Solution optimum initial thresholds and weights Analysis of Genetic Algorithms The optimal initial threshold value is assigned to the right network prediction BP network training BP network prediction Empty the environment variable)

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.)