资源列表
shearlet地震信号处理
- 提供了shearlet工具箱，为shearlet应用在地震信号处理方面提供了帮助
CA-CFAR_mtkl
- 实现了CA-CFAR算法仿真，得到其检测门限及检测概率曲线。并使用基于蒙特卡洛仿真方法得到检测门限及检测概率曲线，与传统的CA-CFAR算法进行了比较。-CA-CFAR algorithm to achieve the simulation, its detection threshold and detection probability curve. And use the resulting Based on Monte Carlo simulation method detection
svd
- svd算法用于滚动轴承故障诊断中，该算法通过对故障信号进行重构，能够有效提高故障频率。-svd algorithm for ball bearing fault diagnosis, fault signal by the algorithm to reconstruct, can effectively improve the fault frequency.
untitled3
- 航天器轨道动力学MATLAB仿真，霍曼转移，Simulink- Spacecraft orbit dynamics MATLAB simulation, Hohmann transfer
modeling1
- 拉格朗日动力学方程自动求解：用于串联、并联、混联机械系统拉格朗日动力学方程建模时自动求解方程的系数-automatic solution of Lagrangian equation
polymax
- 频域内的模态参数识别方法之多参考点最小二乘复频域法，完全自己编写，希望有同样做模态参数识别的前辈给予指教与帮助-Frequency domain modal parameter identification methods as much as the reference point complex frequency domain least-squares method, completely on my own to write, want to have to do the same m
Someclassic-matlab-program
- 一些经典的matlab程序源码有MATLAB图像处理实现螺纹识别、频谱分析加汉宁窗函数、蒙特卡洛法求椭圆面积、RBF神经网络的训练等-There are some classic matlab program source code matlab image processing to achieve a thread identification, spectrum analysis of Hanning window function, Monte Carlo method for elli
sixuanyi
- MATLAB绘制的四旋翼飞行器simmechanics图，用于分析和仿真四旋翼运动学和动力学。但是需要自己在simmechanics加载驱动-MATLAB to draw four rotor aircraft simmechanics figure for four rotor kinematics and dynamics analysis and simulation.But need to himself in the simmechanics load driver.
kriging-surrogate
- Kriging代理模型适用于设计变量和试验样本较多的数据，建立设计变量与数据结果之间的数据模型，可用作进一步的优化设计-Kriging model applies to multiple design variables and large samples of data , can be used for further optimizational design
stochasticResonance
- 随机共振作出SNR信噪比随噪声强度变化的曲线-matlab code for stochastic resonance！used for plotting the figure about SNR versus D（noise strength）
hfssmatlab_source_editing
- 针对阵列天线的多端口馈电设置，可通过该例程进行HFSS的source editing的一次性操作。
optim
- matlab自带的最优化工具箱，用于常见的线性及非线性优化，期待和大家分享。-The matlab optimization toolbox for common linear and nonlinear optimization, looking forward to share with you.