DOA常用算法之一:Capton算法, 也叫做MVDRE ( Minimum Variance Distorionless Response Estimator ),是計算Power specturm
標簽： Distorionless Estimator Variance Response
上傳時間： 2014-01-16
上傳用戶：王者A
Generates a static Minimum-Variance Huffman code tree.，詳細給出了HUFFMAN碼樹構造
標簽： Minimum-Variance Generates Huffman static
上傳時間： 2013-12-21
上傳用戶：1101055045
Capon提出MVDR（Minimum Variance Distortionless Response）高分辯方位估計方法以來，MVDR以其簡單的算法、良好的性能得到了廣泛關注，是一種具有良好實用前景的數字波束形成方法。
標簽： Distortionless Variance Response Minimum
上傳時間： 2014-01-19
上傳用戶：1079836864
Matlab program to plot efficient frontier and minimum variance portfolio
標簽： efficient portfolio frontier variance
上傳時間： 2014-01-24
上傳用戶：lnnn30
This program uses Markowitz portofolio theory to find combination of stocks in a portfolio which has minimum variance for expected returns
標簽： combination portofolio Markowitz portfolio
上傳時間： 2017-05-07
上傳用戶：李彥東
In this project we analyze and design the minimum mean-square error (MMSE) multiuser receiver for uniformly quantized synchronous code division multiple access (CDMA) signals in additive white Gaussian noise (AWGN) channels.This project is mainly based on the representation of uniform quantizer by gain plus additive noise model. Based on this model, we derive the weight vector and the output signal-to-interference ratio (SIR) of the MMSE receiver. The effects of quantization on the MMSE receiver performance is characterized in a single parameter named 鈥漞quivalent noise variance鈥? The optimal quantizer stepsize which maximizes the MMSE receiver output SNR is also determined.
標簽： mean-square multiuser receiver project
上傳時間： 2014-11-21
上傳用戶：ywqaxiwang
方差分析（analysis of variance，或縮寫ANOVA）又稱變異數分析，是一種應用非常廣泛的統計方法。其主要功能是檢驗兩個或多個樣本平均數的差異是否有統計學意義，用以推斷它們的總體均值是否相同。
上傳時間： 2013-12-24
上傳用戶：wab1981
2^x mod n = 1 acm競賽題 Give a number n, find the minimum x that satisfies 2^x mod n = 1. Input One positive integer on each line, the value of n. Output If the minimum x exists, print a line with 2^x mod n = 1. Print 2^? mod n = 1 otherwise. You should replace x and n with specific numbers. Sample Input 2 5 Sample Output 2^? mod 2 = 1 2^4 mod 5 = 1
標簽： mod satisfies minimum number
上傳時間： 2015-06-02
上傳用戶：qlpqlq
This program demonstrates some function approximation capabilities of a Radial Basis Function Network. The user supplies a set of training points which represent some "sample" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the variance for the network. Using the training samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to approximate the function between the given "sample" points.
標簽： approximation demonstrates capabilities Function
上傳時間： 2014-01-01
上傳用戶：zjf3110
Classify using the minimum error criterion via histogram estimation of the densities
標簽： estimation the criterion densities
上傳時間： 2015-08-28
上傳用戶：wang0123456789
