density theorem 中文意思是什麼
density theorem
解釋
密度定理-
The second part consist of chapter four. in chapter one, we study the energy density of harmonic map from finsler manifold and generalize classical result in [ se ]. in chapter two, we obtain lower estimates for the first eigenvalue of the laplace operator on a compact finsler manifold, and it generalize lichnerowicz - obata theorem [ li ] [ ob ]. in chapter three, we derive the first and second variation formula for harmonic maps between finsler manifolds. as an application, some nonexistence theorems of nonconstant stable harmonic maps from a finsler manifold to a riemannian manifold are given
第一章討論finsler流形到黎曼流形調和映射的能量密度的間隙性，推廣了[ se ]中的結果。第二章對緊致finsler流形上laplace運算元的第一特徵值的下界作了估計，推廣了黎曼流形上的lichnerowicz - obata定理[ li ] [ ob ] 。 -
Because the return rate has many good characters such as the following, we estimate the return rate by use of the characters. theorem 1 [ 20 ] set kernel function k ( u ) and density function f ( x ) satisfy the following conditions
第三部分研究在收益率不服從正態分佈的情況下用核估計方法對股票的收益率密度做出估計，然後算出在期滿日時的股價，再用非參數估計方法對歐式看漲期權價值進行評估 -
The strong deviation theorems are new type theorems established by using the notion of the likelihood ratio. professor liu wen frist applied an analysis method in solving a class of strong deviation theorems for a sequense of random variables. later professor liu wen studied the shannon - mcmillan theorem in information theorems [ 2 ] - [ 8 ] and deviation theorems of non - negative continuous random variables [ 10 ] - [ 11 ] by using the analytic technique and obtained some strong deviation theorems. the chapter 2 of the paper studied a class of strong deviation theorems of function of two variables of information sources and obtained a further study of shannon - mcmillan theorem of markov information sourses by definning the using concept of entropy density divergence. the chapter 3 of the paper studied a class of strong deviation theorems of non - negative continuous random variables by using tool of transformation of laplace. information theory, as a branch of applied probability theory, becomes more and more important in appling
劉文教授在解決大數定律中，用首創的分析方法得到一類隨機變量序列的強偏差定理。后來，劉文教授把分析方法用於信息論中shannon - mcmillan定理和連續型隨機變量的偏差定理的研究，得到了若干強偏差定理。本文的第二章是引進任意信源相對熵密度偏差的概念，並利用這個概念研究任意信源二元函數的一類強偏差定理，得到了馬氏信源shannon - mcmillan定理的一個推廣。 -
At the same time we abroad some results of [ 1 ]. by introducing the concept of [ 3 ] about gr - linearly compact discrete ( gr - l. c. d. ), we have proved density theorem in semigroup graded module, and gain a equivalence : for, sqa is gr - injection if and only if for, rq is gr - linearly compact discrete ( gr - l. c. d. )
第四部分證明了半群分次模上的密度定理，並給出了一個等價條件，即：對任意的s s ， _ sq _ a是分次內射的，當且僅當對任意的， _ rq _是分次線性離散緊的，記作： gr ? 1 . c . d -
The paper combines artificial neural network, fuzzy inference system, system identification, and does a thorough research on fuzzy - neural network. the research results are as follows : in this paper, we propose a new neuro - fuzzy systems with laplace ( probability density function ) membership function, and proved its universal approximation property by using weierstrass theorem. we get excellent modeling results for nonlinear systems by applying the new neuro - fuzzy model
本文融合了人工神經網路、模糊推理系統、系統辨識等理論，並圍繞神經網路和模糊推理的結合體? ?模糊神經網路，展開了深入地研究，主要完成了如下研究工作：本文提出一種新型的帶laplace （概率密度函數）型隸屬函數的模糊神經網路模型，並應用微分中值定理和weierstrass定理證明它的通用逼近性。 -
The main results : theorem 1. 2. 2 the function ( u ) - satisfies the integro - differential equation where, theorem 1. 3. 2 if * ( ) s analytic on the complex plane except for the roots of in second chapter, we consider a risk process in which inter - arrival times have a phase - type ( 2 ) distribution, a distribution with a density k ( t ) satisfying the following second order linear differential equation : the conditions are satisfied for all convolutions of two exponential distributions ( with not necessarily equal means )
定理1 . 3 . 2若函數~ * （ ）除了方程（ 1 . 3 . 1 ）的極點外，在整個復平面上都是解析的，則ddas 。 type風險模型是風險理論中應用比較廣泛的一類風險模型dicksonandhipp （ 1998 ）研究了索賠時間間隔服從erlang間分佈的情況，並於2001年aun了其罰金折現期望滿k一積分微分方g
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