资源列表
tangent stiffness method
- ABAQUS umat tangent stiffness method
05 竞争神经网络与SOM神经网络
- 里面有两个神经网络原理的PPT,竞争神经网络和SOM神经网络，还有一个实际案例————矿井突水水源的判别。(There is a neural network principle of PPT, competitive neural network and SOM neural network, there is also a practical case - the identification of mine water inrush.)
matlab
- 对一个信号文件导入并进行fft变换，显示图形；对信号是否平稳，周期性，采样点数N，有偏无偏处理的估计效果等都做了处理和显示(A signal file is imported and transformed by FFT to display graphics; whether the signal is stable, periodic, sampling point N, the estimation effect of biased and unbiased processing are
RBF、GRNN和PNN神经网络案例
- 能够实现RBF,GRNN和PNN神经网络的案例，一个是RBF-近红外光谱汽油辛烷值预测，GRNN，PNN-鸢尾花种类识别。代码和数据均有，直接可以拿来使用。(These are cases of RBF, GRNN and PNN neural networks can be realized. One is RBF-NIR spectroscopy gasoline octane prediction, GRNN, PNN-Iris species identification. Both c
zzz
- 能够完整实现R语言的主成分分析的经典实例，希望能有所帮助(Realization of principal component analysis in R language)
maantdlence
- 这是关于kalman的程序,对学习kalman的人会有一定的帮助(This is a program about kalman, which will be of some help to people who learn about kalman.)
加入权重系数
- 为了抑制两电平逆变器输出中产生的共模电压，本算法采用在代价函数中加入相应的权重系数使得共模电压得到抑制(In order to suppress the common-mode voltage generated in the output of two-level inverters, the algorithm uses the corresponding weight coefficient in the cost function to suppress the common-mode
8078349
- 用MATLAB实现模拟立方体的重构,是计算机视觉中三维重构的重要的应用实验(Simulating cube reconstruction with MATLAB is an important application experiment of 3D reconstruction in computer vision.)
SurrogateOptimizationModule_FileExchange
- 代理优化工具箱，里面有很多非常实用的代理模型(surrogate for large data optimitation and some common used model like the polynomial and others)
MATLAB模拟卫星运动
- 用MATLAB仿真GPS卫星运动，仿真GPS卫星的可见性(Simulation of GPS satellite motion with MATLAB,Visibility of simulated GPS satellites)
theoretical program
- 给予多层膜理论计算棱镜式SPR传感器的反射曲线(Calculating reflectance spectra of SPR sensors with the multi layers reflection theory)
Andrew Ng machine-learning-ex4
- 吴恩达机器学习课程源码，第4个练习作业代码(Andrew machine learning course source code, the fourth practice code)