资源列表
kalman
- 1960年，卡尔曼发表了他著名的用递归方法解决离散数据线性滤波 问题的论文。从那以后，得益于数字计算技术的进步，卡尔曼滤波器 已成为推广研究和应用的主题，尤其是在自主或协助导航领域。-In 1960, Kalman published his famous recursive solution using discrete data linear filtering problem papers. Since then, figures to benefit from advances
12f
- 提高光纤光栅传感器测量精度的研究.Improving the optical fiber grating sensor measuring accuracy o-Improving the optical fiber grating sensor measuring accuracy o
MS_Regressmarkov
- 马尔科夫机制转换模型代码,可以用于货币政策传导机制的拟合与预测.-Markov state switching models are a type of speci cation which allows for the transition of states as an intrinsic property of the econometric model. Such type of statistical representations are well known and utilis
Beamforming
- Its MVDR beamforming technique
ezwPspiht
- ezw图像压缩算法和spiht图像压缩算法-The SPIHT function in this toolbox are listed as follow: func_SPIHT_Demo_Main-- Main function func_SPIHT_Eec-- Encoder func_SPIHT_Dec-- Decoder func_DWT-- Wavelet decomposition func_InvDWT-- Inverse wavelet e
the-researh-of-pH-control-strategies
- 关于国内PH研究,其中包括ph值的预测控制，线性反馈，模糊控制-the lastest research of ph,and it contains MPC and linear control
kalman_intro_chinese
- 此是国外机器人实验室的中国留学生翻译的kalman滤波的教程，较为介绍了扩展kalman滤波理论，同时给出具有代表意义的例子，帮助理解-err
12x9_firm_mult
- dsp_mult coding..megafunction wizard: ALTMULT_ADD // GENERATION: STANDARD // VERSION: WM1.0 // MODULE: ALTMULT_ADD -dsp_mult coding..megafunction wizard: ALTMULT_ADD // GENERATION: STANDARD // VERSION: WM1.0 // MODULE: ALTMULT_ADD
turbine3
- 实现含风电场RX模型的系统潮流计算，风电场采用RX模型，此模型充分考虑风力发电机的输出功率特性，比其他模型完善，在模型中将迭代过程分为两步:常规潮流迭代计算和异步风力发电机的滑差迭代计算。- In short, the algorithm carried out to simulate the wind farmas an RX bus is as follows: 1)Begin with s=snorm in each machine, snorm being the rated s
shili2
- matlab 6.0 时尚百例的21-30的代码，基本函数的应用-Fashion matlab 6.0 code 21-30 of 100 cases, the basic function of the application
VB
- 测量时，导线控制网的平差计算，数据处理。-Measurement, the wire network adjustment calculation, and data processing.
pattern
- 杜达的模式分类图书中的程序源代码，可以很好地帮你理解模式分类中的各种方法-Pattern Classification Duda books in the program source code, you can very well help you understand the various methods of pattern classification