markov models 中文意思是什麼

markov models 解釋
馬可夫模型
  1. Hidden markov models have been widely used for modeling sequences of weakly dependent random variables, with applications in as such as speech processing, neurophysiology and biology

    隱馬爾可夫模型可應用於弱相依隨機變量的建模上，也可用作研究發音過程、神經生理學與生物遺傳等方面的工具。
  2. The laboratory has proposed several speaker recognition methods involving computational auditory models, modular neural networks, gaussian mixed models, hidden markov models, and implemented a recognition framework combining semantic and voiceprint information

    實驗室提出了基於聽覺計算模型、模塊化神經網路、高斯混合模型、隱馬爾科夫模型等說話人識別方法，以及結合語義和聲紋信息的說話人識別框架。
  3. The three basic problems of two - dimensional ( 2 - d ) bidden markov models ( hmms ) are studied, including probability evaluation, optimal states and parameter estimation

    摘要研究了2維隱馬爾可夫模型的三個基本問題，包括概率評估問題、最優狀態問題和參數估計問題。
  4. Because wavelet transformation has strong capability of withstanding noise, we have easily considered making wavelet transformation for the input signals, and recognizing them in hidden markov models

    由於小波變換具有很強的去噪功能，一個自然的想法是在hmm識別系統前串連一個小波濾波器，將待識別模式信號作小波變換后再輸入hmm模式識別系統以進行識別。
  5. 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。
  6. Multiple alignment analysis based on hidden markov models

    基於隱馬爾可夫模型的多重序列分析
  7. Pfam is a large collection of multiple sequence alignments and hidden markov models covering many common protein families

    為一包含了許多多重序列校準，以及「隱藏式馬爾科夫模型」的巨大集合，裏面涵蓋了許多常見的蛋白質家族。
  8. I analyze the relationships between transition probabilities and transition intensities by markov and semi - markov models, and then discuss the estimation of transition intensities. part 3 : the payable probabilities

    通過markov和semi - markov模型，分析了健康、疾病、死亡三狀態間轉移概率和轉移強度之間的關系，並從實驗的角度分析了對轉移強度的估計方法。
  9. This dissertation introduces the characteristics of health insurance. it analyses the relationships between transition probabilities and transition intensities in markov and semi - markov models, and discusses the payable probabilities and the net single premium under different insurance contracts. finally, it tries to study the calculation and the sensitivity testing of total premium

    本文在對健康保險產品的特徵進行分析的基礎上，介紹了現有的健康保險的markov模型，探索性地引入semi - markov模型研究轉移概率和轉移強度的關系，並探討了健康保險的賠付概率，以及典型的健康保險產品的純保費的計算。
  10. Based on the " application on faults diagnosis of rotating machine in hidden markov models " ( national nature science fund project, no : 50075079 ), the hidden markov models ( hmms ) dynamic pattern recognition theories and methods are studied, then proposed the applications in faults diagnosis of rotating machine by hmm methods and developed the faults diagnosis software based on hmm

    本文以國家自然科學基金項目「基於隱markov模型的旋轉機械故障診斷新方法的研究」 （編號： 50075079 ）為基礎，提出的博士學位論文題目為「 hmm動態模式識別理論、方法以及在旋轉機械故障診斷中的應用」 。本文以大型旋轉機械為研究對象，研究了hmm動態模式識別理論與方法在旋轉機械故障診斷中的應用，開辟了旋轉機械計算機輔助故障診斷的新途徑。
  11. Ion single channel signal restoration and parameters ' est imation based on the hidden markov models

    模型的離子單通道信號恢復及參數估計
  12. Singular value feature and hidden markov models - based face detection

    基於奇異值特徵和隱馬爾可夫模型的人臉檢測
  13. Chapter two introduced the basic ideas of markov chain theories and hidden markov models ( hmm ). then the theories and algorithms of hmm are studied aid the application of speech recognisition uses hmm. at last, attempt to use hmm to faults diagnosis

    第二章：介紹了hmm的基本理論、演算法；通過對語音識別問題和旋轉機械故障診斷問題的比較分析，論述了hmm應用於旋轉機械故障診斷的可行性。
  14. 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。
  15. In this article some mathematical methods and their applications, including the counting of words, the method of composition analysis, and hidden markov models, are presented for biological sequences

    論文介紹生物序列研究中的計數方法、組分分析方法、隱馬爾可夫模型方法以及它們的某些應用。
  16. At last, combining the related knowledge of wavelet theory and hidden markov models, we introduce wavelet transformation for nonparametric estimation of hmm ' s and discuss how to choose resolving scale of haar - wavelet orthogonal series " estimation

    最後，結合小波理論和隱馬爾科夫模型的相關知識，將小波變換應用到隱馬爾科夫模型非參數估計問題中來，並探討了其中haar小波正交級數估計量分解尺度的選取。
  17. In the field of audio recognition, with many mature and creative technologies applying, especially the hidden markov models ( hmm ), the effect and efficient of the audio recognition system have been enhanced. but due to the mismatch between training and testing environment ( such as background, audio transition channel ), the recognition systems based on hmm tends to drastically degrade in performance

    在音頻信號識別特別是語音識別領域內，隨著隱馬爾科夫模型（ hmm ）的應用，使得系統的識別性能有了改進，但是由於訓練和測試環境（背景噪聲、音頻傳輸通道等）的失配常常導致識別性能的嚴重下降。
  18. Chapter2 : traditional time series models and multivariate fuzzy time series models. the chapter introduces the vector arma model, transfer arima model, seasonal arima, and arima model of traditional time series models, and two - factors models, heuristic models, and markov models of multivariate fuzzy time series models. i devise the process of the model construction, and propose the findings

    本章介紹傳統時間數列模型（向量arma模型、 arima轉移函數模型、季節性arima模型以及arima模型）與多變量模糊時間數列三種模型?二因子模型（ two - factormodels ） 、引導式模型（ heuristicmodels ） 、馬可夫模型（ markovmodels ） ，模型建構步驟與流程，及傳統時間數列模型轉換為多變量模糊時間數列模型過程，並分別針對多變量模糊時間數列三種模型提出本研究不同於先前研究之處。
  19. This thesis explored the application of the forecasting methods of arima time series and multivariate fuzzy time series : two - factors models, proposed by chen and hwang ( 2000 ), heuristic models, proposed by huamg ( 2001 ), and markov models, proposed by wu et. al. ( 2003 ). this thesis employed five to sixteen intervals to instead of the method proposed by huarng ( 2001 )

    本文的研究重點在探究近期理論界提出的三種多變量模糊時間數列模型? ? chen和hwang （ 2000 ）所提出的二因子模型、 huarng （ 2001 ）所提出的引導式模型、 wu等（ 2003 ）所提的馬可夫模型，分別針對各模型的建構步驟、適用場合，及上述文獻未達到的部份，再做深入研究，並比較其結果。
  20. The paper introduces the concept of fail - safe, the hardware and software ' s frame, divides the faults into some classes, and lists some general faults. i introduce the principle of reliability, fault forecast and it ' s standards firstly. secondly, i analyze the fmea / ca, fault tree, event tree and markov models in detail, deepen its theory and describe the flows of these models

    文章介紹了故障-安全的概念、 hj04a系統的硬體和軟體結構，對系統故障進行了分類，並列出了一些常見的故障；結合可靠性理論、故障預測的概念和標準，詳細分析了fmea ca 、故障樹、事件樹、 markov故障模型，對這些理論進行了一定的深化，描述了每個故障模型的故障處理流程，並對其進行了評價；介紹了故障診斷的概念，分析了故障診斷專家系統和k層bp神經網路，提出了一種層次結構圖論法的故障診斷方法。
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