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cma
说明： cma自适应恒模算法，由接受信号估计信道的自适应恒模算法(cma constant modulus adaptive algorithm, to receive signals from the adaptive channel estimate constant modulus algorithm)
- 2009-07-30 10:55:46下载
- 积分：1
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OFDMbooksimulation
OFDM的完整仿真，其中仿真了OFDM的波形，有完整的D/A转换，还有详细的解释(Complete OFDM simulation)
- 2013-07-16 09:50:35下载
- 积分：1
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JAKEmode
衰落信道JAKE 模型,对理解衰落信道有好处.(JAKE fading channel model for understanding Fading good.)
- 2006-12-17 23:34:01下载
- 积分：1
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Kalman
卡尔曼滤波,用于加深理解卡尔曼滤波器的设计并认识扩展卡尔曼滤波器EKF和无损变化滤波器的优点(kalman filter，EKF and UKF)
- 2014-08-30 17:15:17下载
- 积分：1
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BLDC_Control
In This Matlab Simulation, We simulate Control System Of BLDC Motor Speed Control With Addaptive Fuzzy Sliding Mode Control And Simulatione Show That The result is better than other Control Method Such as Conventional PID.
- 2013-02-18 23:04:46下载
- 积分：1
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migration
基于matlab平台,实现地震波场延拓并偏移归位源码.地震波偏移是地震资料处理中重要步骤之一,利用此程序可以模拟地震波传播过程,并实现偏移归位至原初反射位置(Matlab-based platform for the realization of seismic wave field extension and migration to the digital source. Seismic wave offset seismic data processing is an important step forward in the use of this procedure can simulate the seismic wave propagation process and realize Homing offset position to the original reflectance)
- 2021-03-23 22:49:15下载
- 积分：1
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0072232153_code
Programs for the book Advanced Engineering Mathematics using MATLAB, 2ndEd.
- 2009-05-09 12:18:07下载
- 积分：1
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filter
包含了小波去噪，BP,NLMS,LMS,RLS等去噪方法，效果不错。(It contains the wavelet de-noising, BP, NLMS, LMS, RLS and other denoising method, good results.)
- 2016-12-12 09:29:37下载
- 积分：1
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Asian-call
Asian call price Asian call priceAsian call priceAsian call price(Asian call price)
- 2013-04-26 02:58:45下载
- 积分：1
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KNN
K最邻近密度估计技术是一种分类方法，不是聚类方法。
不是最优方法，实践中比较流行。
通俗但不一定易懂的规则是：
1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。
2.选出最小的前K数据个距离，这里用到选择排序法。
3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method.
It is not the best method, but it is popular in practice.
Popular but not necessarily understandable rule is:
1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov).
2. select the minimum distance from the previous K data, where the choice sorting method is used.
3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)
- 2020-10-23 14:37:22下载
- 积分：1
