• 제목/요약/키워드: Almost convergence

검색결과 409건 처리시간 0.025초

DLMS 알고리즘의 수렴에 관한 연구 (Almost-Sure Convergence of the DLMS Algorithm)

  • Ahn, Sang Sik
    • 전자공학회논문지B
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    • 제32B권9호
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    • pp.62-70
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    • 1995
  • In some practical applications of the LMS Algorithm the coefficient adaptation can be performed only after some fixed delay. The resulting algorithm is known as the Delayed Least Mean Square (DLMS) algorithm in the literature. There exist analyses for this algorithm, but most of them are based on the unrealistic independence assumption between successive input vectors. Inthis paper we consider the DLMS algorithm with decreasing step size .mu.(n)=n/a, a>0 and prove the almost-sure convergence ofthe weight vector W(n) to the Wiener solution W$_{opt}$ as n .rarw. .inf. under the mixing unput condition and the satisfaction of the law of large numbers. Computer simulations for decision-directed adaptive equalizer with decoding delay are performed to demonstrate the functioning of the proposed algorithm.m.

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ALMOST SURE CONVERGENCE FOR WEIGHTED SUMS OF NA RANDOM VARIABLES

  • BAEK J. I.;NIU S. L.;LIM P. K.;AHN Y. Y.;CHUNG S. M.
    • Journal of the Korean Statistical Society
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    • 제34권4호
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    • pp.263-272
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    • 2005
  • Let {$X_n,\;n{\ge}1$} be a sequence of negatively associated random variables which are dominated randomly by another random variable. We discuss the limit properties of weighted sums ${\Sigma}^n_{i=1}a_{ni}X_i$ under some appropriate conditions, where {$a_{ni},\;1{\le}\;i\;{\le}\;n,\;n\;{\ge}\;1$} is an array of constants. As corollary, the results of Bai and Cheng (2000) and Sung (2001) are extended from the i.i.d. case to not necessarily identically distributed negatively associated setting. The corresponding results of Chow and Lai (1973) also are extended.

WEAK CONVERGENCE FOR STATIONARY BOOTSTRAP EMPIRICAL PROCESSES OF ASSOCIATED SEQUENCES

  • Hwang, Eunju
    • 대한수학회지
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    • 제58권1호
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    • pp.237-264
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    • 2021
  • In this work the stationary bootstrap of Politis and Romano [27] is applied to the empirical distribution function of stationary and associated random variables. A weak convergence theorem for the stationary bootstrap empirical processes of associated sequences is established with its limiting to a Gaussian process almost surely, conditionally on the stationary observations. The weak convergence result is proved by means of a random central limit theorem on geometrically distributed random block size of the stationary bootstrap procedure. As its statistical applications, stationary bootstrap quantiles and stationary bootstrap mean residual life process are discussed. Our results extend the existing ones of Peligrad [25] who dealt with the weak convergence of non-random blockwise empirical processes of associated sequences as well as of Shao and Yu [35] who obtained the weak convergence of the mean residual life process in reliability theory as an application of the association.

CONVERGENCE THEOREMS AND STABILITY PROBLEMS OF THE MODIFIED ISHIKAWA ITERATIVE SEQUENCES FOR STRICTLY SUCCESSIVELY HEMICONTRACTIVE MAPPINGS

  • Liu, Zeqing;Kim, Jong-Kyu;Kim, Ki-Hong
    • 대한수학회보
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    • 제39권3호
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    • pp.455-469
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    • 2002
  • The Purpose Of this Paper is to introduce the concept of a class of strictly successively hemicontractive mappings and construct certain stable and almost stable iteration procedures for the iterative approximation of fixed points for asymptotically nonexpansive and strictly successively hemicontractive mappings in Banach spaces.

HV가 다른 0.57 wt.% 탄소강의 미소 균열에 따르는 피로 한도 (Fatigue Limit According to Microcracks in 0.57 wt.% Carbon Steel with Different HV)

  • 오창석;구경희;이금화;남기우
    • 한국산업융합학회 논문집
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    • 제25권2_2호
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    • pp.233-238
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    • 2022
  • This study evaluated the fatigue limit depending on microcracks using 0.57 wt.% carbon steel with different Vickers hardness. The fatigue limit was almost constant up to a certain limit regardless of the carbon content. However, the fatigue limit decreased rapidly as the size of the crack increased. As the crack aspect ratio was smaller, the fatigue limit of the depth (point A) a lot decreased. The fatigue limit ratio of the depth decreased significantly because the crack propagation in the depth direction was fast as the crack aspect ratio became smaller. On the other hand, the fatigue limit ratio of surface cracks increased as the crack aspect ratio decreased.

도시쓰레기를 이용한 활성탄의 제조 (Manufacturing of active carbon used city waste)

  • 상희선;타몬 하지메
    • 한국산업융합학회 논문집
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    • 제9권1호
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    • pp.87-91
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    • 2006
  • This research exposes the process of carbonization of solid waste from the city and the method of activated, as well as the process of meso-porous under several test conditions. In addition, it is expected that the active carbon obtained through this process can be used as filter for air and sewage treatment, and can absorb dioxin(as almost 60-80% of meso-porous and porous properties area) unlike the existing active carbon.

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에너지 키에 기초한 대형 칼라 영상 기록물의 계층적 인덱싱 기법 (Energy Keys Based Scalable Indexing Schemes of Large Color Image Archives)

  • 박대철;강종묵
    • 융합신호처리학회 학술대회논문집
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    • 한국신호처리시스템학회 2001년도 하계 학술대회 논문집(KISPS SUMMER CONFERENCE 2001
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    • pp.225-228
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    • 2001
  • 대형 칼라 영상 기록물의 효과적인 계위적 인덱싱 특성을 갖는 방법을 제안하였다. 1차와 2차로 나누어진 칼라 키와 에너지 키 방법은 고속 검색 시스템의 성능을 발휘하며 질의 응답 시간면에서 DB의 크기에 별로 영향을 받지 않는 거의 일정한 응답 시간 특성을 갖는다.

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학습시간을 개선한 Fuzzy c-means 알고리즘 (The Enhancement of Learning Time in Fuzzy c-means algorithm)

  • 김형철;조제황
    • 융합신호처리학회 학술대회논문집
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    • 한국신호처리시스템학회 2001년도 하계 학술대회 논문집(KISPS SUMMER CONFERENCE 2001
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    • pp.113-116
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    • 2001
  • The conventional K-means algorithm is widely used in vector quantizer design and clustering analysis. Recently modified K-means algorithm has been proposed where the codevector updating step is as fallows: new codevector = current codevector + scale factor (new centroid - current codevector). This algorithm uses a fixed value for the scale factor. In this paper, we propose a new algorithm for the enhancement of learning time in fuzzy c-means a1gorithm. Experimental results show that the proposed method produces codebooks about 5 to 6 times faster than the conventional K-means algorithm with almost the same Performance.

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