有效并行演算法 的英文怎麼說

中文拼音 [yǒuxiàobīnghángyǎnsuànfǎ]
有效并行演算法 英文
efficient parallel algorithm
  • 有 : 有副詞[書面語] （表示整數之外再加零數）: 30 有 5 thirty-five; 10 有 5年 fifteen years
  • 效 : Ⅰ名詞（效果; 功用） effect; efficiency; result Ⅱ動詞1 （仿效） imitate; follow the example of 2 ...
  • 行 : 行Ⅰ名詞1 （行列） line; row 2 （排行） seniority among brothers and sisters:你行幾? 我行三。where...
  • 演 : 動詞1 （演變; 演化） develop; evolve 2 （發揮） deduce; elaborate 3 （依照程式練習或計算） drill;...
  • 算 : Ⅰ動詞1 （計算數目） calculate; reckon; compute; figure 2 （計算進去） include; count 3 （謀劃;計...
  • 法 : Ⅰ名詞1 （由國家制定或認可的行為規則的總稱） law 2 （方法; 方式） way; method; mode; means 3 （標...
  • 有效 : effective; valid; efficacious
  1. 31 grama a y, gupta a, kumar v. isoefficiency : measuring the scalability of parallel algorithms and architectures

    并行演算法的性能評測包括有關并行演算法的加速比效率可擴放性等的評測方法。
  2. With the effective parallel computation algorithm, the simulation of complex inviscid flow is finally achieved more efficiently and quickly

    進一步配合流場有效的并行計算演算法，最終可實現高效、快捷地模擬復雜流場。
  3. Considering the characters of bp neural network, such as the simple structure, the advisable malleability, self - fitness, self - studying, nonlinear function approximating, the considerable abilities of parallel computing, fault - tolerant and so on, the bp algorithm have been extensively applied to the areas of system modeling, pattern recognition and seismic exploration since 1986. compared with other algorithms, as the above reasons, the bp algorithm has become the most usual and efficient solutions to the artificial neural networks

    由於人工神經網路中的bp神經網路結構簡單，可塑性強，具有良好的自適應、自學習、極強的非線性逼近、大規模并行處理和容錯能力等特點，自1986年rumelhart等人提出以來，被廣泛應用於系統建模、模式識別、地震勘探等重要領域。而bp演算法數學意義明確，步驟分明，是神經網路中最為常用、最有效、最活躍的一種方法。
  4. Ptwma is an effective successful algorithm and model to the knowledge discovery of the multiple streams time series

    Ptwma為分散式，并行控掘多流時間序列提供了一種有效的演算法和模型。
  5. The first algorithm is low precise but simple and credible, the second is high precise but complex and incredible. 4 ) developed four kinds of methods aimed to improve precision and credibility of navigation system. the first is parallel sandia inertia terrain - aided navigation ( psitan ) ; the second is tercom + sitan, it can restrain two important disadvantages of sitan ; the third is particle filter - based terrain - aided navigation ( pftan ), the particle filter can reduce the error of navigation ; the last is tercom + pftan, where tercom is looked as monitor to ensure the credibility of navigation system

    採用并行sitan方法來提高導航精度，並克服奇異值問題；提出了tercom + sitan方法，綜合利用兩者的優點，在保持sitan導航精度的前提下，有效地克服了sitan的兩個缺點；提出了一種基於連續蒙特卡洛濾波（常被稱為particlefilter ）的地形匹配演算法（ pftan ） ，有效地克服了利用sitan時由於地形隨機線性化帶來的誤差，使導航精度有較大的提高；提出了tercom作為監視器的地形輔助導航思想，並將其應用到連續蒙特卡洛方法上，較大地增加了系統的可靠性和精度。
  6. Two block time - recursive algorithms are developed for the efficient and fast computation of the 1 - d rdgt coefficients and for the fast reconstruction of the original signal from the coefficients in both the critical sampling case and the oversampling case. the two algorithms are implemented respectively by a unified parallel lattice structure. and the computational complexity analysis and comparison show that the proposed algorithms provide a more efficient and faster method for the computation of the discrete gabor transforms

    首先論證了一維rdgt系數求解演算法和由變換系數重建原信號演算法，不論是在臨界抽樣條件下還是在過抽樣條件下，都同樣具有塊時間遞歸特性，並提出了相應的塊時間遞歸演算法及其并行格型結構實現方法，計算機模擬驗證了并行格型結構實現的可行性，計算復雜性分析與比較也說明了rdgt塊時間遞歸演算法的并行格型結構在計算時間方面所具有的高速和高效性能。
  7. Theebe - cg algorithm does not require construction of the global matrix. it can be implemented efficiently on a massively parallel architecture

    Ebe - cg演算法不需要構造全局矩陣，它在大規模并行結構中能被有效地實現。
  8. The cg algorithm does not require construction of the global matrix. it can be implemented efficiently on a abstract massively parallel architecture

    共軛梯度演算法不需要構造全局矩陣，它在大規模并行結構中能被有效地實現。
  9. In this text, we first do some research on the genetic algorithm about clustering, discuss about the way of coding and the construction of fitness function, analyze the influence that different genetic manipulation do to the effect of cluster algorithm. then analyze and research on the way that select the initial value in the k - means algorithm, we propose a mix clustering algorithm to improve the k - means algorithm by using genetic algorithm. first we use k - learning genetic algorithm to identify the number of the clusters, then use the clustering result of the genetic clustering algorithm as the initial cluster center of k - means clustering. these two steps are finished based on small database which equably sampling from the whole database, now we have known the number of the clusters and initial cluster center, finally we use k - means algorithm to finish the clustering on the whole database. because genetic algorithm search for the best solution by simulating the process of evolution, the most distinct trait of the algorithm is connotative parallelism and the ability to take advantage of the global information, so the algorithm take on strong steadiness, avoid getting into the local

    本文首先對聚類分析的遺傳演算法進行了研究，討論了聚類問題的編碼方式和適應度函數的構造方案與計算方法，分析了不同遺傳操作對聚類演算法的性能和聚類效果的影響意義。然後對k - means演算法中初值的選取方法進行了分析和研究，提出了一種基於遺傳演算法的k - means聚類改進（混合聚類演算法） ，在基於均勻采樣的小樣本集上用k值學習遺傳演算法確定聚類數k ，用遺傳聚類演算法的聚類結果作為k - means聚類的初始聚類中心，最後在已知初始聚類數和初始聚類中心的情況下用k - means演算法對完整數據集進行聚類。由於遺傳演算法是一種通過模擬自然進化過程搜索最優解的方法，其顯著特點是隱含并行性和對全局信息的有效利用的能力，所以新的改進演算法具有較強的穩健性，可避免陷入局部最優，大大提高聚類效果。
  10. Genetic algorithm, as a computational model simulating the biological evolution process of the genetic selection theory of dar - win, is a whole new global optimization algorithm and is widely used in many fields with its remarkable characteristic of simplicity, commonability, stability, suitability for parallel processing, high - efficiency, and practibility. on the other hand, there are many op - timization problems in the field of digital image processing, such as image compression, pattern - recognition, image rectification, image segmentation, 3d image recovery, image inquiry, and or so. in fact all these problems can be generalized as the problem of searching for a global optimal solution in a large solution space, which is the classic application field of genetic algorithm

    遺傳演算法是模擬達爾文的遺傳選擇和自然淘汰的生物進化過程的計算模型，是一種新的全局優化搜索演算法，具有簡單通用、穩定性強、適于并行處理以及高效、實用等顯著特點，在很多領域得到了廣泛應用，另一方面，在圖像處理領域有很多優化問題如圖像壓縮，模式識別，圖像校準，圖像分割，三維重建，圖像檢索等等，實際上都等同於一個大范圍搜索尋優問題，而最優化問題是遺傳演算法經典應用領域，因此遺傳演算法完全勝任在圖像處理中優化方面的計算。
  11. In chapter 4 we discuss the design of the high speed and high performance vlsi and its imp1ementation, firstly we ana1yze and compare the features and ru1es of al1 kinds of fft algorithm, adopt complex radix 4 butterfly calcu1ation as basic alu, then discuss all kinds of process architectures, the design thoughts, rule, method, technique way, the characteristics of the design are r4 dit algorithm, pingpong ram design method and pipeline structure between stages. we also analyze the limited word length effect and the method to avoid overflow of the fixed points fft process, bring out the expandable platform mode

    第四章主要討論了高速高性能的快速傅立葉變換處理器的設計和實現，首先分析和比較了各種快速傅立葉變換演算法的特性和規律，提出基4蝶算的演算法具有最好的性價比，討論了順序、級聯、并行和陣列的處理結構，闡述了設計高速高性能快速傅立葉變換處理器時的設計原則、設計思路、所採用的技術路線，驗證並測試fft處理器，分析了定點fft處理過程由於有限字長效應所產生的量化誤差的范圍及防溢出控制辦法，提出了可擴展平臺模式。
  12. The results above have many important applications in analyzing the efficiency ( complexity ) of fast algorithms and parallel algorithms designed by half - cutting of recurrence technology

    上述結果在分析用減半遞推技術設計的快速演算法和并行演算法的效率（復雜性）分析方面有重要應用。
  13. In this thesis, we adopt loosely coupled mode, develop a series of effective parallel atpg algorithms based on sequential g - f two - value tg algorithm and hope fs algorithm

    本文基於松耦合模式，以g - f二值tg演算法和hopefs演算法為基礎，快速開發了一系列有效的并行atpg演算法，獲得了良好效果。
  14. Considering the np - complete problem, how to get the approximate optimized scheme of job - shop scheduling, and aimed at improving the efficiency of products and taking good advantage of concurrence, asynchronism, distributing and juxtaposition in multi - products and devices processing, we could divide the working procedures into the attached one which has the only precursor and subsequence and unattached one by analyzing working flow chart of job - shop, that is the working procedures are divided into two types, then the bf and the ff methods about memory scheduling in os are applied, therefore a new approximate optimized scheme is presented in the paper which could solve the common job - shop scheduling. namely, the acpm and the bfsm are applied to the classified and grouped working procedures considering the compact of the procedures and practical examples approved it. the results we analyzing and tested show that it is better than the heuristic algorithm common used, for less restriction terms, more satisfying algorithm complexity and better optimized results

    針對job - shop調度問題求最優解演算法這一npc問題，本文以充分發揮多產品、多設備加工所具有並發性、異步性、分佈性和并行性的加工優勢，從而提高產品的加工效率為目標，對job - shop調度問題的工藝圖進行適當分解，使工序在一定時間段或是為具有唯一緊前、緊后相關工序或是為獨立工序，即將工序分兩類，再結合操作系統中內存調度的最佳適應（ bf ）調度方法和首次適應（ ff ）調度方法的先進思想，通過分析提出了一種解決一般job - shop調度問題的全新近優解方案：在考慮關鍵設備上工序盡量緊湊的前提下，將工序分類、對這兩類工序分批採用擬關鍵路徑法（ acpm ）和最佳適應調度方法（ bfsm ）安排工序的演算法，用實例加以驗證，並給出結果甘特圖。
  15. Some methods are used to increase the parallel computation efficiency, such as the data cyclic distribution, overlapping of communication and computation, load - balancing in accordance with the computation ability of the dawn - 2000 node, communication mergence and so on

    採用循環帶狀劃分、計算與通信重疊、根據曙光2000機結點計算能力分配任務量、合併通信等技術有效地提高了并行演算法效率。
  16. The multiscale model is not only capturing the several important ways in which a data analysis or signal processing problem can have multiscale characteristic, but also leading to an efficient and highly parallelizable algorithm for optimal estimation of stochastic processes

    此模型的的建立不僅是獲取具有多尺度特徵的數據分析或信號處理問題的一種重要方式，同時，利用它還可以為最優估計隨機過程的狀態變量誘導出高度有效、并行迭代演算法。
  17. The algorithm of upgrading of general face recognition system is based on all serial algorithm in a single computer, it is very slow to deal with a large amount of data, and the efficiency is low. so, this article introduces the application of grid computing in face recognition system, upgrades the original serial algorithm of face data into parallel algorithm in the grid platform by using mpi parallel program, realizes the already existing updated algorithm of the recognition of face to be processed in the distributed computers, which has strengthened the systematic ability to deal with a large amount of data, in order to improve systematic performance

    常規人臉識別系統中的更新演算法都是基於單機的串列演算法，在處理大量數據的時候速度慢，效率低，介紹了網格計算在人臉識別系統中的應用，把原來的人臉數據更新串列演算法改為并行演算法並通過編寫mpi并行程序移植到該網格計算平臺中運行，實現了原有人臉識別系統中更新演算法的分散式處理，增強了系統處理大量數據的能力，以達到提高系統性能的目的。
  18. In this paper, we explore the effectiveness and extend one such formal methodology in the design of massively parallel algorithms

    本文將這樣的方法進行推廣，並探索這種方法在設計密碼學大量并行演算法中的有效性。
  19. One is the performance of data mining algorithms degrades, the other is many distance - based and density - based algorithms maybe not effective. these problems can be solved by the following methods : l ) transport the data from high dimensional space to lower dimensional space by dimensionality reduction, then process the data as lower dimensional data. 2 ) to improve the performance of mining algorithms, we can design more effective indexing structures, adopt incremental algorithms and parallel algorithms and so on

    解決的方法可以有以下幾種：一個可以通過降維將數據從高維降到低維，然後用低維數據的處理辦法進行處理；對演算法效率下降問題可以通過設計更為有效的索引結構、採用增量演算法及并行演算法等來提高演算法的性能；對失效的問題通過重新定義使其獲得新生。
  20. Finally, genetic optimization research is summarized on several typical production scheduling problems. after expounding the general idea of genetic algorithm, the comparative advantages in contrast to the traditional algorithm, the basic characteristics of genetic algorithm and its theoretical base, the paper puts emphasis on the efficiency of genetic algorithm in the scheduling of flow shop, and puts forward an improving genetic algorithm : the ordinal genetic algorithm based on the heuristic rules. the new algorithm introduces into the initial group the solution of heuristic algorithm, and in the group structure adopts a strategy of first ordering according to the priority of the adaptive solution, and then defining a new way of choosing probability by segments, which provides more hybridizing opportunity for optimized individuals, and designs variation - control rule to prevent single population and partial optimal solution

    在論述了遺傳演算法的思想、與傳統搜索演算法的比較優勢、遺傳演算法的基本特徵和遺傳演算法的理論基礎（包括模式定理、隱含并行性、基因塊假設、欺騙問題和收斂性定理）后，重點探討了遺傳演算法在flowshop調度問題中的潛力和有效性；結合啟發式規則，提出了一個改進的遺傳演算法?基於啟發式規則的有序遺傳演算法，新演算法在初始種群中引入了啟發式演算法的解，在種群結構上採用了先按適應值優劣排序再分段確定選擇概率的新策略，使優質個體有更多的雜交機會，在變異中設計了變異控制規則，以防種群單一化，而陷入局部優化解。
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