markov number 中文意思是什麼
markov number
解釋
馬爾可夫方程-
In the chapter two we discussed that the server would first use speed - 1 to serve customers when the system entered the busy state from the empty state, but when the server found the number of customers in the system exceeded the thresh - n during serving, after finishing the service of current customer it would use speed - 2 to serve the next customer till there is no customer. by the method of supplementary variable, l - transition and constructing vector markov, we attained the distribution of the queue length, the distribution of wait - time, the distribution of stay - time, the utility and etc. in the last part of this chapter, we discussed the optimal n * for thresh n which minimizing the cost function and we illustrate the cost function behaves for various parameter selections by a numerical study
在本文第二章討論了當系統從空閑進入忙期時是服務臺以速度1進行服務，但一旦對某顧客服務完畢時如發現系統中的顧客數超過n值時就以速度2服務后續顧客直到系統變空的可修排隊系統，通過構造各種向量馬氏過程和吸收向量馬氏過程，獲得了瞬態、穩態隊長分佈、等待時間分佈、逗留時間分佈、更新周期分佈等一系列排隊指標以及可用度、可靠度等一些可靠性指標，在本章最後又從系統如何更好節省費用角度出發討論了門限n的最優取值問題，並利用mathematic軟體對費用函數進行了數值模擬。 -
A markov model is used for calculation of dcf throughput. it is disclosed that the key parameters are the number of contending stations and the length of contention window. the maximum throughput and the best parameters are offered by theoretical analysis
對dcf部分，利用markov模型推導網路吞吐量性能，指出競爭站點數和競爭窗口長度是關鍵性能參數，從理論上給出最大吞吐量和最優參量值。 -
As to the stochastic simulation of stochastic biological processes, if only use stochastic petri net, although it has standard graphics expression, which is isomorphic to markov chain, along with the increase of models ’ scale and complexity, the number of states will increase exponentially, so it is very difficult to analyze models by the method which stochastic petri net has ; if only use stochastic algorithm, which has rapid simulation speed, but lack of intuitive graphical expression
對于隨機生物過程的模擬，如果只採用隨機petri網模擬生物隨機過程，其優點是形象、直觀，缺點是隨著模型的規模和復雜性的增加，狀態的數量呈指數性地增長，出現模型狀態空間的爆炸問題，用隨機petri網本身的分析方法很難分析整個系統的性能;如果只採用隨機模擬演算法模擬，其優點是速度較快，但是缺少形象的圖形表達，不利於模擬技術的應用。 -
Secondly, to optimize the system ' s settings, some important issues about the recognition of short - term speech recognition are discussed, such as the state number of markov chains, the scale of training - set, the gauss mixture number, etc. thirdly, we have deeply studied the noise - restrained parameters
深入研究和分析了參數級的抗噪問題，通過實驗分析比較了靜態特徵參數以及高階動態參數之間的抗噪性能，得出了一般噪聲環境下性能相對較好的特徵參數形式。 -
In this paper, we introduce a new framework for statistical signal processing based on wavelet - domain hidden markov models ( hmms ) that concisely models the statistical dependencies and nongaussian statistics encountered in real - world signals, since the wavelet transform can decorrelate image data by reducing the number of states of wavelet coefficients, thus making wavelet - domain hmms manipulable and usefu l for statistical image modeling
本文介紹了一種統計信號處理的框架模型，這種模型是基於小波域的隱馬爾可夫模型，它可以簡潔地對實際生活中遇到的信號的統計相關性和非高斯統計進行建模。因為小波變換可以通過減少小波系數的狀態數量來去除圖像數據的相關性，因此小波域的隱馬爾可夫模型對于統計圖像建模是可以掌控的和有用的。 -
Markov chains are special random process. renently, professor liu wen, who first invented the analysis method in solving large number theory, connected it and markov chains and got some limit properties about one - order markov chains
劉文教授在大數定律的研究中把首創的分析方法和馬氏鏈結合起來，得出一系列有關一重馬氏鏈的極限結果，本論文繼續並拓廣這方面的研究，相應於一重馬氏鏈，二重馬氏鏈也有其相應的極限性質。 -
The major tasks include : ( 1 ) expand the schema theorem for ga. the schema theorem with binary coding advanced by professor holland is expanded to limited integer, letter, floating point numbers the number of which value is limited, and their hybrid coding. ( 2 ) put forward replacing by the excellent chromosome ga ( recga ), superiority colony first ga ( scfga ) and improve the ga ; ( 3 ) make probability convergence analysis of recga using the theory of markov chain, random process ; ( 4 ) make convergence analysis of scfga using the principle of contractive mapping in functional analysis theory ; ( 5 ) design the test programs ( cap ) to resolve np problems ( course arrangement ) with gas ; based on recga, modify the arithmetic and then conduct tests
主要有以下幾方面工作： （ 1 ）將二進制編碼遺傳演算法的模式定理擴展到由有限整數、字母或取值個數有限的浮點數編碼，或它們混合編碼的遺傳演算法范圍； （ 2 ）提出最佳個體替換策略遺傳演算法（ recga ） 、優勢群體優先策略遺傳演算法（ scfga ） ，對遺傳演算法進行改進； （ 3 ）使用隨機過程理論markov鏈對recga進行了收斂性分析； （ 4 ）使用泛函分析理論壓縮映射原理對scfga進行了收斂性分析； （ 5 ）使用遺傳演算法設計了解決np類問題（排課問題）的測試程序（ cap ） ，並根據recga對演算法進行改進並進行測試。
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