隨機系統 的英文怎麼說

中文拼音 [suíjīxìtǒng]
隨機系統 英文
probabilistic system
  • 隨 : Ⅰ動詞1 （跟; 跟隨） follow 2 （順從） comply with; adapt to 3 （任憑; 由著） let （sb do as he li...
  • 機 : machineengine
  • 系 : 系動詞（打結; 扣） tie; fasten; do up; button up
  • 統 : Ⅰ名詞1 （事物間連續的關系） interconnected system 2 （衣服等的筒狀部分） any tube shaped part of ...
  • 隨機 : random stochasticrandom
  • 系統 : 1. （按一定關系組成的同類事物） system 2. （有條理的;有系統的） systematic
  1. The problem of dual control is studied for the stochastic system of which the uncertainty model is polytopic

    摘要針對不確定性模型為多胞型的隨機系統進行了對偶控制的研究。
  2. Mean square stabilization of t - s fuzzy stochastic systems

    模糊隨機系統的均方鎮定
  3. The equations of the mean value functions and the covariance functions are established for dynamical systems whose inputs are fuzzy stochastic processes. an existence and uniqueness theorem of ito fuzzy stochastic differential equations is proved, some explicit representations of solutions and the equations of statistical characteristics are deduced for linear fuzzy stochastic differential equations, and numerical methods to nonlinear fuzzy stochastic differential equations are proposed, the conditions for stability and observability of fuzzy linear systems are derived. the kalman filter algorithms of linear fuzzy stochastic systems are brought forward

    主要成果包括：提出了模糊隨機變量協方差和反向協方差的概念；研究了二階模糊隨機變量的均方收斂性，並在此基礎上得到了均方模糊隨機分析、平穩模糊隨機過程及其譜分解的若干定理；根據均方模糊隨機分析理論，得到了輸入為模糊隨機過程的線性系統的輸出輸入統計特徵關系方程；證明了ito型模糊隨機微分方程解的存在唯一性，並給出了ito型線性模糊隨機微分方程解的表達式，統計特徵方程以及非線性模糊隨機微分方程的數值解法；得到了模糊線性系統的穩定性和可觀性條件、線性模糊隨機系統統計特徵方程和線性模糊隨機系統的kalman濾波演算法；研究了當觀測值是模糊數據時，線性回歸模型的建立。
  4. Wavelet analysis of a sort of multidimension stochastic system

    一類高維隨機系統的小波分析
  5. If the input or output ( observation ) variables of a stochastic system are fuzzy, the system becomes a stochastic system on fuzzy number space, and is called a fuzzy stochastic system

    當一個隨機系統的輸入或輸出（觀測）變量取值為模糊數，它就成為取值于模糊數空間上的隨機系統，簡稱模糊隨機系統。
  6. Sensitivity of reliability in nonlinear random systems with independent failure modes

    非線性隨機系統的獨立失效模式可靠性靈敏度
  7. The method of multiple scales is used to determine the equations of modulation of amplitude and phase. the steady state response can be obtained by solving a couple of algebraic equations, which have been achieved by careful deduction under some conditions. and because of the complexity of the equations, programs are necessary to solve the equations mentioned above, and certain graphs are presented. based on chapter two, in chapter three, the method of multiple scales is introduced to the study of the multiple - dimensional nonlinear stochastic systems under random external excitation

    在第二章基礎上，第三章將多尺度法引入到相應的隨機系統的研究中；嚴格推導了系統的約簡方程，用矩方法求出穩態解應滿足的方程，獲得一些結果；並且數值模擬結果與理論推導的結果是一致的；並注意到，與其對應的確定性系統相比較，系統響應從周期解變為近似周期解，系統的相軌線從極限環變為擴大的近似極限環；隨著激勵帶寬的增大，此擴大的近似極限環的寬度將增大。
  8. Keywords : industrial engineering, industrial management, operations research, management sciences, production systems, operations management, supply chain management, management of technology, quality engineering and management, reliability engineering, six - sigma, lean manufacturing and lean enterprise, information systems engineering and management, enterprise management systems engineering, semiconductor manufacturing, health care systems, data mining, financial engineering, simulation, optimization, decision sciences, stochastic systems ， probability and statistics

    關鍵詞：工業工程，工業管理，運籌學，管理科學，生產系統，運作管理，供應鏈管理，技術管理，質量工程和管理，可靠性工程，六西格馬，精益製造與精益企業，信息系統工程與管理，企業管理系統工程，半導體製造，醫療系統，數據挖掘，金融工程，系統模擬，優化，決策科學，隨機系統，概率和統計。
  9. Time domain 1 ) the problem of the estimability of stochastic systems is discussed by analyzing information varying between states and state estimate errors. an information theoretic definition of estimability for general stochastic systems is given in the sense of minmax entropy estimation. it is concluded that a linear gaussian stochastic system is estimable if and only if the corresponding linear definite system is observable

    時域： 1 ）通過分析系統狀態估計中先驗和后驗信息變化討論了隨機系統的可估計性問題，給出在最小最大熵估計意義下一般系統的可估計性定義，並揭示出線性高斯系統的可估計性與相應確定性系統的可觀測性之間存在互為充分必要條件的關系。
  10. ( 2 ) the strong tracking extended kalman filter is extended to a class of nonlinear time - varying stochastic systems with additive combined colored noise

    （ 2 ）將強跟蹤擴展卡爾曼濾波器演算法推廣到一類加性復合有色噪聲干擾下的非線性時變隨機系統。
  11. 2 ) by analyzing the information and conditional information description mechanism of system states, the problem of stochastic model reduction is investigated based on state aggregation. the information loss and conditional information loss between the full - and reduced - order models are measured by entropy, while the independence and conditional independence within me components of aggregated state are measured by kullback - leibler information distance. several model reduction methods for stable and unstable linear systems are derived by employing two criteria to get aggregation matrices : the minimal information loss and the maximal independence

    2 ）分析了隨機系統狀態空間模型中的信息和條件信息描述機制，以shannon熵為手段描述線性系統模型降階過程中的信息和條件信息損失，以kullback - leibler信息作為衡量降階模型狀態向量各分量之間統計獨立性的測度，針對穩定和不穩定系統研究基於狀態集聚的模型降階問題：分別運用最小信息損失準則和最大獨立性原則，得出幾種狀態集聚的信息論方法，並討論降階模型的性質、階次的確定、系統噪聲分佈特性等問題。
  12. Abstract : the order reduction problem of the system state feedback and output feedback controllers is discussed according to the inclusion princip l e of linear discretetime stochastic systems. two kinds of conditions, restrictio n and aggregation, are presented for the controller order reduction. by designing the order reduced feedback controller for a 9order system, the properties of th e reduced order controllers and all order controllers are discussed

    文摘:根據線性離散隨機系統的包含原理1 ，研究了系統的狀態反饋和輸出反饋控制器的降階問題.給出了控制器降階兩種類型：聚集和約束.通過一個9階系統控制器的設計，討論降階控制器和全階控制器的性能
  13. Finally, the results are compared with periodic and random chains. then we studied acoustic wave propagation in 1d quasiperiodic and aperiodic systems by means of he transfer matrix. transmission rate, reflection rate, energy flow, logarithmic energy flow, energy density and lyapunov exponent are computed numerically, and compared with periodic and random system

    其次研究了聲波在幾種一維準周期和非周期系統中的傳播，通過轉移矩陣的方法，數值地得到了系統的傳播系數t _ n 、反射系數r _ n 、能流密度j _ n ，能量密度e _ n和lyapunov指數，給出了以上各量與傳播長度n以及頻率之間的關系，同時發現能流及能量密度都具有分形結構，並與周期和隨機系統的結果作了比較。
  14. Thus finally we can realize the minimum variance control. simulations show that preferable parameter estimation and satisfactory control performance can be achieved using the derived dual adaptive control

    利用卡爾曼濾波對未知的系統參數進行估計，最終實現隨機系統的對偶自適應控制。
  15. System with specific coupling schemes is discussed as an example to illustrate the synchronization time of non - linear stochastic dynamical systems

    最後用一類具有特定耦合機制的隨機系統作為例子來說明非線性隨機動力系統的同步時間問題。
  16. Convergences of the sequence of fuzzy random variables are discussed, and then some theorems on m. s. fuzzy stochastic analysis and stationary fuzzy stochastic processes are proved

    本文第一部分研究模糊隨機系統的建模與分析的若干問題，推廣了隨機系統理論的一些結果。
  17. We introduce a two - dimensional motion model of neural system, fhn neural model. then by using of adiabatic elimination method and the presence of noise, the system becomes a special double singular stochastic system. the effects of parameter, which marks the singular degree of stochastic at singular point, on the phase transition of the fhn model is discussed when is non - integer

    首先介紹了神經系統的二維動力學模型，即fhn模型，接著考慮引入乘性噪聲后， fhn神經模型變為一個特殊的雙奇異隨機系統，研究了該系統的相變情況，同時得出了噪聲強度臨界值的解析表達式，著重分析了噪聲的奇異性標度和噪聲強度對神經細胞內電位的概率分佈以及電位的平均值的影響。
  18. This thesis is devoted to the evolutionary random response problems of linear random systems, and to the response problems of the random duffing system due to harmonic excitations

    論文重點研究了線性隨機系統在演變隨機激勵下的響應問題，和初步探索隨機duffing方程在諧和激勵下的一些非線性現象。
  19. Signal estimation algorithm for discrete stochastic systems with multiplicative noise is mainly researched in this dissertation

    本文主要研究多通道帶乘性噪聲的離散隨機系統的信號最優估計演算法。
  20. In this thesis single input - single output and multiple input - multiple output stochastic systems are discussed respectively. innovations are introduced to reconstruct the original minimum variance control problem of stochastic system, which is unsolvable by means of dynamic programming. so it can be converted into multiple single - step control problems, in which kalman filter is used to estimate unknown system parameters

    本文分別針對單輸入單輸出和多輸入多輸出的隨機系統進行了研究，通過引入系統的新息對原不可解的動態規劃問題進行重構，將系統參數隨機變化的最小方差控制問題轉化成為多個基於新息的單步控制問題。
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