资源列表
noise-reduction--RLS
- 基于rls算法的多麦克风降噪matlab程序代码-Matlab code based on the the rls algorithm of multi-microphone noise reduction
3MFCC
- 自己用的一个求MFCC参数的程序，绝对好用，带原始数据，可直接运行-A program and MFCC parameters, absolutely easy to use, with the original data, and can be run directly
The-multivariable-PID-tuning
- 多变量PID调谐功率单元机组的非线性控制-Multivariable PID tuning Power Generating Unit and nonlinear control
Several-nonlinear-control-methods
- 基于多变量PID调节锅炉汽机单元的几种非线性控制方法-Several nonlinear control methods based on multivariate PID regulator boiler turbine unit
Denoise_w_Mod_sim
- 声发射信号小波模极大值去噪，去噪效果还算不错-Acoustic emission signal wavelet modulus maxima denoising own series, exhibitions
erweixiaobobianhuan
- 2维小波变换经典程序-此示意程序用DWT实现二维小波变换-2-dimensional wavelet transform classic program- this indicated the procedure using DWT realization of two-dimensional wavelet transform
mallatxiaobopu
- 小波谱分析mallat算法经典程序-此示意程序用D实现小波谱分析-Small spectral analysis the Mallat algorithm classic program- This indicated the small spectral analysis procedures D
xiaozaoxiaobo
- 用于小波消噪-此函数用于研究Mallet算法及滤波器设计-此函数用于消噪处理-- This function is used to study Mallet filter algorithm design- This function is used to de-noising processing for wavelet denoising
xiaoboxiaozaochonggou
- 小波滤波器重构-此函数用于研究Mallet算法及滤波器设计-The wavelet filters reconstruction- This function is used to filter algorithm design and research Mallet
daixiaoboshiyichengxu
- 2代小波示意程序-此程序用提升法实现第二代小波变换-我用的是非整数阶小波变换-采用时域实现,步骤先列后行-2 Generation Wavelet indicated procedures- This procedure is used to upgrade method second generation wavelet transform- I use a non-integer order wavelet transform- time-domain implementation step
ditui
- 这个程序是递推最小二乘法，，每取得一次新的观测数据后，就在前一次估计结果的基础上，利用新引入的观测数据对前次估计的结果，根据递推算法进行修正，从而递推地得出新的参数估计值-This procedure is recursive least squares method, for each new observation data, the previous estimated on the basis of the results, the introduction of the new obs
yiwangyizi
- 有遗忘因子的最小二乘，递推最小二乘的缺点是常常出现数据饱和，新加入的数据对参数向量的更新作用不大，加如遗忘因子可很好的解决这一问题-Forgetting factor least squares, recursive least squares drawback is often saturated data, new data on the update of the parameter vector, such as the forgotten factor can be a good so