弱收斂序列 的英文怎麼說

中文拼音 [ruòshōuliǎnxùliè]
弱收斂序列 英文
weakly convergent sequence
  • 弱 : Ⅰ形容詞1 （氣力小; 勢力差） weak; feeble 2 （年幼） young 3 （差; 不如） inferior 4 （接在分數或...
  • 收 : Ⅰ動詞1 （把攤開的或分散的事物聚集、合攏） put away; take in 2 （收取） collect 3 （收割） harvest...
  • 斂 : Ⅰ動詞1 （收起; 收住） hold back; keep back 2 （約束） restrain 3 （收集; 徵收） gather; collect; ...
  • 列 : Ⅰ動1 （排列） arrange; form a line; line up 2 （安排到某類事物之中） list; enter in a list Ⅱ名詞1...
  • 收斂 : 1 （減弱或消失） weaken or disappear 2 （約束言行） restrain oneself 3 [數學] convergence; constr...
  1. Weak convergence of the distribution of independent random variable series in hilbert space

    空間中獨立隨機變量序列分佈的弱收斂性
  2. This paper consists of two parts : in the first part, we will discuss the prob - lem of the pth - mean, complete consistency for the estimators of a nonparamet - ric and linear model with l ~ p - mixingale errors ; in the second part, we will dis - cuss the problem of the rth - mean 、 complete consistency for the estimators of themodels above with weak stationary linear process errors and the uniformly mean consistency. to the nonparametric model y _ ni = g ( x _ ni ) + _ ni, 1 i n, let g _ n ( x ) = w _ ni ( x, w _ n1, … ? xnn ) y _ ni estimate the unknown function g ( x ). to the linear model y _ i - x _ i1 1 + … ? + x _ iq ? _ q, we use lse _ nj to estimate the unknown parametric _ j

    本篇論文主要是由兩大部分內容構成：一是關于誤差是l ~ p ?混合序列的線性回歸模型參數的最小二乘估計與非參數回歸模型未知函數的權函數估計的p ~ -階平均相合性和完全收斂性問題；另一部分是關于誤差是弱平穩線性過程的線性模型參數的最小二乘估計與非參數回歸模型未知函數的權函數估計的r ?階平均相合性和完全收斂性以及權函數估計的一致平均相合性問題。
  3. Limit theorems for the integration of function sequence with respect to weak convergence probability measure sequence

    函數序列關於弱收斂概率測度序列積分的極限定理
  4. The topological convergence of the cone weak subdifferential of set - valued mapping sequence

    集值映射序列的錐弱次微分的拓樸收斂性
  5. It comes up with a new notion, d - solution, which is applied to the distance estimation, by virtue of hilbert space ; furthermore, the dissertation has gained a necessary condition which is identity of minimum mean - square value in linear function classes, so that d - solution extends minimum mean - square value within the domain of nonlinear function equation or equation system ; and, the dissertation studies in detail the classical moment estimation and maximal likelihood estimation on the parameters of ar ( p ), a series of theorems in the estimation section shows the moment estimators are consistent on the ground of large samples jikewise, those distribution functions of the estimated parameters accord to maximum likelihood estimation converge gauss distribution if the white noise is gaussan

    首先，藉助hilbert空間理論，提出了距離估計的d -解，給出了d -解的必要條件，這個條件在線性函數類里即是極小二乘估計法， d -解的必要條件滿足的方程實質上將極小二乘估計法推廣到多函數及非線性函數類。再而，詳細地研究了多元弱平穩序列自回歸模型ar （ p ）的參數經典的矩的替代估計和極大似然估計，獲得矩的替代估計的一致性的結果。對基於gauss白噪聲假設多元弱平穩序列自回歸模型的均值、白噪聲的協方差陣的極大似然估計都有依分佈收斂到多元正態分佈的統計性質。
  6. Radial basis function neural network ( rbfnn ) is chosen to build predictive model. rbfnn is a special type of neural network linear - in - weight in nature and having nonlinear processing properties. finally, an adaptive filter is applicable to do the followed weak signal extraction work

    接著選用徑向基函數神經網路（ radialbasisneuralnetwork ， rbfnn ）建立混沌時間序列預測模型，徑向基函數神經網路是一種局部逼近的人工神經網路，訓練簡潔而且學習收斂速度快，能夠逼近任意非線性函數，最後將預測誤差送入自適應信號分離器進行處理，檢測出微弱信號。
  7. Then the model is simplified, the theory of martingale, simulation, and diffusion approximations are discussed firstly. these methods are applied in the model. then get some useful results, so we can estimate the upper bound for the ruin probability and the approximation of the finite time ruin probability

    並詳細的討論了模型有限時間內破產概率和最終破產概率的估計，應用隨機過程序列弱收斂，鞅以及隨機模擬等理論，得出一些有意義的結果? ?在有限時間內破產概率的逼近表達式；最終破產概率的上界和有限時間內破產概率上界；有限時間內破產概率的隨機模擬演算法；並得到最終破產概率滿足的泛函方程。
  8. In this paper, the numerical solution of the nonlinear equations is studied, under the point estimates or weak condition the dominating sequence is constructed, at the same time, the existence and the convergence theorem is obtained and its proof is given

    本文主要研究非線性方程f （ x ） = 0的數值解法，在點估計和弱條件下，構造了優序列，給出了存在性收斂性定理及其證明。
  9. In the first chapter, we used the method of majoring sequences to studied the convergences of newton ' s methods of " reducing the counting of derivative " and " without inversing of derivative under weak conditions "

    在第一章中，用優序列方法研究了減少導映照計值次數和避免導映照求逆的牛頓迭代在弱條件下的收斂性。
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