基于pid模糊预测

matlab实现人体回波模型创建和短时傅里叶测试(Matlab realization of human echo model and short time Fourier test)

发源于美国麻省理工学院的Vensim软件，是由Ventana公司开发，在全球和国内获得最广泛使用系统动力学建模软件。它具有图形化的建模方法，除具有一般的模型模拟功能外，还具有复合模拟、数组变量、真实性检验、灵敏性测试、模型最优化等强大功能。Vensim有Vensim PLE, PLE Plus, Professional和DSS版本，适合不同的用户。(Vensim software, originated from Massachusetts Institute of Technology, was developed by Ventana company, and has gained the most extensive use of system dynamics modeling software both globally and domestically. It has graphical modeling methods. Besides the general model simulation function, it also has many powerful functions such as compound simulation, array variables, authenticity test, sensitivity test, model optimization, and so on. Vensim has Vensim, PLE, PLE, Plus, Professional, and DSS versions for different users.)

说明：  使用python语言实现复杂网络SIR和SIS模型编程(SIR AND SIS model with python language)

三维比例导引法仿真，可以攻击静止/机动目标(Three dimensional proportional navigation simulation can attack stationary / maneuvering targets)

估算锂电池soc的MATLAB simulink模型(MATLAB Simulink model for estimating SOC of lithium battery)

说明：  新的屈服准则ABAQUS子程序UMAT，含有多种较新的准侧，能很好运用(The new yield criterion ABAQUS subroutine UMAT contains a variety of newer standards and can be used very well.)

极限学习机用于数据建模，可定性定量。代码有相关解释，直接运行。(The extreme learning machine is used for data modeling and can be qualitatively quantified.The code has an explanation and runs directly.)

说明：  本程序是完成网络的评价路径长度和聚类系数的计算。(This program is to calculate the average path length and clustering coeffient of a network.)

多目标优化的最大妥协解，转换成单目标优化 三人博弈的部分(The maximum compromise solution of multiobjective optimization)

大坝变形的精确预测对其安全评价方面发挥着重要作用。为了进一步提高大坝变形预测精度，提出了一种改进的灰狼算法优化支持向量机的预测模型。通过引入非线性收敛因子和采用动态加权策略，提升灰狼算法优化支持向量机惩罚因子和核函数参数的能力，并以优化的参数来建立支持向量机大坝变形预测模型。选取实例数据，在与布谷鸟算法、差分进化算法、粒子群算法、基本灰狼算法优化的支持向量机预测比较中，实验结果表明，改进的灰狼算法对支持向量机参数的优化是有效的，可以提高大坝变形预测精度。(Accurate prediction of dam deformation plays an important role in its safety evaluation. In order to further improve the prediction accuracy of dam deformation, an improved grey Wolf algorithm is proposed to optimize the prediction model of support vector machine. By introducing the nonlinear convergence factor and using the dynamic weighting strategy, the gray wolf algorithm is improved to optimize the capacity of the support vector machine penalty factor and the kernel parameter, and the support vector machine dam deformation prediction model is established with the optimized parameters. The experimental results show that the Improved Grey Wolf algorithm is effective for the optimization of support vector machine parameters, and it can improve the accuracy of the deformation prediction of the dam.)