各種功率譜估計(jì)算法!包括AR譜估計(jì),BURG算法,YULE-WALK方程
標(biāo)簽： YULE-WALK BURG 功率譜 估計(jì)算法
上傳時間： 2014-01-02
上傳用戶：希醬大魔王
現(xiàn)代譜估計(jì)用萊文森-德賓(Levinson-Durbin)算法求解尤利-沃克(YULE-WALKer)方程。 形參說明： r:雙精度實(shí)型一維數(shù)組，存放YULE-WALKer方程的元素r(0),r(1),...r(p)。 p:AR模型階數(shù)。 a:AR模型系數(shù)a(0),a(1),...a(p)。 v:預(yù)測誤差功率
標(biāo)簽： Levinson-Durbin YULE-WALKer 譜估計(jì) 算法
上傳時間： 2014-01-26
上傳用戶：ippler8
該程序源碼能夠很好地用Levinson算法求解YULE-WALKer方程以得到 階AR模型的參數(shù) 。
標(biāo)簽： YULE-WALKer Levinson 程序源碼 AR模型
上傳時間： 2016-04-08
上傳用戶：aa54
無線通信的各種運(yùn)動模型。適用于移動通信、無線傳感器網(wǎng)絡(luò)等領(lǐng)域。 包括：Random walk、random waypoint、random direction、boundless simulation area、 gauss-markov等運(yùn)動模型 - probabilistic random walk
標(biāo)簽： random simulation direction boundless
上傳時間： 2014-11-12
上傳用戶：libinxny
譜估計(jì)（建立二階AR模型）、利用FFT求解功率譜估計(jì)、利用AR模型的YULE-WALKer方程求解模型參數(shù)等
標(biāo)簽： YULE-WALKer FFT AR模型 譜估計(jì)
上傳時間： 2017-01-11
上傳用戶：gdgzhym
ACM 10596 Morning Walk
標(biāo)簽： Morning 10596 Walk ACM
上傳時間： 2014-01-13
上傳用戶：yzhl1988
walk random process programmed in visual c++ 0.6
標(biāo)簽： programmed process random visual
上傳時間： 2014-06-26
上傳用戶：shawvi
C++ From Scratch: An Object-Oriented Approach is designed to walk novice programmers through the analysis, design and implementation of a functioning object-oriented application using C++. You will learn all the critical programming concepts and techniques associated with the language in the context of creating a functioning application. Best selling C++ author Jesse Liberty shows you how to create "Decryptix", a game of decoding a hidden pattern as quickly as possible, using nothing but successive guesses and the application of logic. Every example and technique is put into the context of achieving a goal and accomplishing an end.
標(biāo)簽： Object-Oriented programmers Approach designed
上傳時間： 2013-12-25
上傳用戶：225588
The algorm of viterbi. You talk to your friend three days in a row and discover that on the first day he went for a walk, on the second day he went shopping, and on the third day he cleaned his apartment. You have two questions: What is the overall probability of this sequence of observations? And what is the most likely sequence of rainy/sunny days that would explain these observations? The first question is answered by the forward algorithm the second question is answered by the Viterbi algorithm. These two algorithms are structurally so similar (in fact, they are both instances of the same abstract algorithm) that they can be implemented in a single function:
標(biāo)簽： discover viterbi algorm friend
上傳時間： 2016-02-16
上傳用戶：xc216
Here we are at the crossroads once again Youre telling me youre so confused You cant make up your mind Is this meant to be Youre asking me Trademark But only love can say - try again or walk away But I believe for you and me The sun will shine one day So Ill just play my part And pray you ll have a change of heart But I cant make you see it through Thats something only love can do Face to face and a thousand miles apart Ive tried my best to make you see Theres hope beyond the pain If we give enough if we learn to trust [Chorus] I know if I could find the words To touch you deep inside Youd give our dream just one more chance Dont let this be our good-bye
標(biāo)簽： crossroads confused telling again
上傳時間： 2016-04-12
上傳用戶：changeboy
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