markov algorithm 中文意思是什麼
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Compared with aitken extrapolation, eigenvalues - based algorithm bypass aitken transform and perform more effectively than aitken extrapolation algorithm theoretically in the process of iterating hyperlink - based markov matrix
與aitkenextrapolation演算法相比，基於特徵值求解的演算法不藉助aitken變換，而通過特徵值直接求解馬爾可夫超鏈接矩陣的主特徵向量，從理論上比aitkenextrapolation演算法更高效。 -
They are expanding model of the bomb body, bursting model of the bomb body and motion model of the fragments. according to the models, the paper gives a detailed algorithm for the whole process of the bomb explosion. ( 7 ) based on the explosion mechanism and the stochastic characteristic of the shell, the paper advances some reasonable hypotheses and supposes that the explosion process of the shell is a markov process, thus constitutes two explosion models of the shell : the imitation model an
（刀從爆炸的機理出發，利用合理的假設，將殼體的爆炸過程處理為馬爾可夫過程，把爆炸的機理同爆炸的隨機性聯系在一起，建立了殼體爆炸的兩種模型:模擬模型和簡化模型，提出了破裂程度的倍密度函數和破裂方向的倍密度函數兩個概念，得到了基於半邊結構的虛擬殼體爆炸過程中任一條邊出現裂縫的概率公式。 -
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。 -
This thesis focuses on techniques of dynamic fault tree in system reliability modeling and its qualitative and quantitative analysis. it studies bdd solution for static sub trees 、 markov chain solution for dynamic sub tree briefly and the modularization of dynamic fault tree ; presents the algorithm for top event occurrence rate of dynamic fault tree based on weibull distribution. then this thesis presents a new approach to solve top event occurrence rate and a new generation algorithm of minimal cut sequence of dynamic fault tree that deviate from markov model completely
本文著眼于動態故障樹在系統可靠性建模及定性定量分析中的技術，研究了基於bdd的靜態子樹分析方法、基於馬爾可夫模型的動態子樹分析方法以及動態故障樹模塊化方法，並提出了基於威布爾分佈的動態故障樹頂事件發生概率計算方法;提出了一種完全脫離馬爾可夫模型的求解動態故障樹頂事件發生概率的方法和一種最小順序割集的生成方法。 -
In this thesis, an algorithm based on multiple features for recognition of escherichia coli promoter was proposed. firstly, word frequency method was utilized to extract the content ’ s information of a given sequence, and position weight matrix and hidden markov model were applied to analyze the information on structure, and then this information was input into a classifier
本文提出了一種基於多特徵的大腸桿菌啟動子判別演算法，即通過詞頻分析獲得序列的組成特徵，利用位置權重矩陣（ pwm ）和隱馬爾科夫模型（ hmm ）獲得序列的結構特徵，然後輸入到一個分類器中進行分類。 -
In order to overcome these faults, we designed a new hybrid genetic algorithm - - simultaneous evolution genetic algorithm ( sega ). this sega is different from traditional ga in evolution manner, and then we use markov modeling to analysis our sega
為了克服這些缺陷，本文設計了一個全新的混合遺傳演算法sega ，這一演算法在進化方式上與傳統的混合遺傳演算法明顯不同，然後，用馬爾可夫鏈的有關知識對sega演算法進行數學分析。 -
Furthermore, a new elastic matching algorithm is designed with the combination of shape blending, which is based on physical elastic model, and a distinct improvement of performance is achieved. chapter 4 is mainly focused on recognition approaches using hidden markov model. firstly, the general concepts and algorithms of hidden markov model are described, and then, a new model called ddbhmm is discussed and compared with the classic model in detail
文中首先介紹了一種自適應形態校正技術，隨后討論了彈性匹配中的一些基本演算法及存在的問題，並在此基礎上研究了一種新的彈性匹配演算法，其主要特點是在匹配演算法中引入了一種基於物理模型的形變度量，能夠有效地改善原有演算法的性能。 -
As an example, the parallel machine scheduling problem is mapped on a non - constrained matrix construction graph, and a aco algorithm is proposed to solve the parallel machine scheduling problem. comparison with other best - performing algorithm, the algorithm we proposed is very effective. the finite deterministic markov decision process corresponding to the solution construction procedure of aco algorithm is illustrated in the terminology of reinforcement learning ( rl ) theory
本章最後提出了解決并行機調度問題的蟻群演算法，該演算法把并行機調度問題映射為無約束矩陣解構造圖，並在演算法的信息素更新過程中應用了無約束矩陣解構造圖的局部歸一化螞蟻種子信息素更新規則，與其他幾個高性能演算法的模擬對比試驗證明這種方法是非常有效的。 -
For the circling moon phase mission, this paper proposes an autonomous optical navigation algorithm using the gauss - markov process and unscented kalman filter
針對繞月軌道段任務，提出了基於高斯-馬爾科夫過程和unscented卡爾曼濾波的自主軌道確定方法。 -
A wavelet domain hidden markov tree ( hmt ) model is constructed to model statistical dependence and nongaussian statistics of wavelet coefficient. the estimate of hmt model parameters can be obtained by em algorithm
該方法通過小波域的隱markov樹（ hmt ）模型來描述小波系數的統計相關性和非高斯性，利用em演算法獲得hmt模型參數的估計。 -
Algorithm study on texture synthesis based on markov causal neighborhood
因果鄰域系統的紋理綜合演算法研究 -
Selecting a proper neighborhood system and using the ability of markov random field to describe spatial dependence, mrf can be used to model the structural and textural behavior of images. selecting a appropriate model and making use of the optimal algorithm ? simulated annealing to estimate the parameters, wonderful image restoration can be achieved
馬爾可夫隨機場能夠很好地描述空間連續性，用適當的鄰域系統，能對圖像的結構特徵建模；結合優化演算法?模擬退火，可以獲得滿意的圖像復原效果。 -
This paper studies 3 kinds of algorithms : the viterbi algorithm, multiresolutional algorithm based on wavelet transformation and bayesian bootstrap algorithm. the viterbi algorithm is based on the hidden markov model theory and it is a kind of map estimation, this paper studies this algorithm and puts up an algorithm that suits for filtering in the presence of interference. multiresolutional algorithm takes full advantage of multiresolutional data, we can see it has a better filtering ability than the traditional filtering methods ; bootstrap algorithm is a recursive bayesian estimation, it describes the probability density function by the samples, so it can be used to nonlinear non - gaussion filtering, the simulation result of the two groundings is presented
Viterbi演算法以隱馬爾可夫理論為基礎，是一種最大后驗概率估計方法，本文對該演算法進行了研究，給出了一種適合於非高斯干擾條件下的濾波方法；多分辨分析方法充分利用到了多解析度測量數據所包含的信息，從模擬結果中可以看出，該方法的濾波精度要高於傳統的濾波演算法；自主濾波方法是一種遞推貝葉斯估計演算法，它利用采樣點來描述目標狀態的概率密度函數，因而適用於非線性、非高斯條件下的濾波，本文分別對這兩種情況下的濾波進行了模擬。 -
In the study of environment model - based processing, the thesis presents a signal enhancement method based on combination of the ocean acoustic propagation, measurement system and noise models. furthermore, a signal enhancement algorithm is developed on the basis of the model - based identifer ( mbid ) implemented by the the augmented gauss - markov process and corrsponding extended kalman filter
本文通過了對模基信號處理實現信號增強的理論研究，提出了一種利用傳播模型、噪聲模型和陣測量模型、並注重環境信息來實現水聲信號增強的方法，繼而實現了基於增廣高斯?馬爾可夫過程和相應的擴展卡爾曼濾波聯合的模基辨識器的信號增強演算法。 -
The aco algorithms are fitted into the framework of generalized policy iteration ( gpi ) in rl based on incomplete information of the markov state. furthermore, we show that the pheromone update in the acs and ant - q algorithm is based on the mc methods or some formalistic combination of mc methods and td methods
此外在強化學習的理論框架內說明了as演算法是一種基於蒙特卡洛方法的強化學習演算法， acs和ant - q演算法是一種蒙特卡洛方法與瞬時差分方法在形式上相結合的強化學習演算法。 -
It can take advantage of the advancement of hmm and gmm, utilize dynamic programming technique to realize the nonlinear time alignment between speech feature vectors and markov state sequences, use expectation - maximum algorithm to re - estimate the gmm parameters and finally employ levenshtein distance to calculate the word error rate between the recognized and expected results
它將隱markov模型和gaussian混合密度分佈緊密聯系，結合動態規劃演算法對時間序列和markov狀態鏈進行非線性時間對齊，並運用em演算法對gaussian混合模型的參數進行重新估計，識別出來的結果與期望結果採用levenshtein距離進行比較並得出其字誤差率。 -
In chapter four this paper proposes a kind of imm algorithm which uses many kinds of models and does some simulations ; a kind of imm algorithm based on time - varying markov transition probabilities matrix is introduced and simulated ; based on the two algorithms above, a new modified imm algorithm which merges many kinds of models and time - varying
在第四章中本文提出了一種使用多種機動運動模型交互的imm演算法並作了模擬;介紹了一種基於時變馬爾可夫轉移概率矩陣的imm演算法並作了模擬;在此基礎上提出了一種改進的imm演算法，演算法結合使用多種運動模型及時變馬爾可夫轉移概率矩陣，模擬結果表明其獲得了對高速高機動目標較好的跟蹤性能。 -
In this thesis, we first introduce give the definition of hidden markov models. then the methods to solve the three basic problems in the application of hidden markov models are introduced, namely three basic arithmetic : forward - backward algorithm, viterbi algorithm, baum - welch algorithm. also we present commonly model of hidden markov processes dynamic system
在這篇論文中，首先給出了隱馬爾科夫模型的定義，接著介紹了隱馬爾科夫模型實際應用中所面臨的三大基本問題的解決方案，即隱馬爾科夫模型三大基本演算法：前向一後向演算法、 viterbi演算法、 baum ? welch演算法。 -
* the sixth chapter presents a tool wear area image segmentation algorithm based on markov random field m odel. the refresh formula of relaxation labeling is deduced. with the algorithm, the segmentation result composed of wear area, background area and tool body area is obtained according to the map ( maximum a posterior ) criterion
第六章以馬爾可夫隨機場理論為基礎，提出刀具磨損區域分割演算法，推導了鬆弛迭代更新公式，並用該方法對圖像分割問題進行求解，獲得了map準則下將刀具圖像的分割結果。 -
Thirdly, this paper gives out a method of discovering markov blanket of multi - sets rather singleton, and proves the correctness of the method, afterwards this paper expresses it using bayes network too. finally, aimed at the application of educational evaluation, this paper performs post - processing at those rules mined by apriori algorithm, and filters those rules with the constraints of conditional independences, and reads out conditional independences from rules
面向教育評估系統中的具體應用，本文提出了對原有系統中採用的apriori演算法挖掘的關聯規則進行后處理的方法：採用條件獨立性和傳統支持度- - -可信度框架相結合的方法進行關聯規則的過濾，並從中發現存在的條件獨立性限制。
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