隨機并行 的英文怎麼說
中文拼音 [suíjībīngháng]
隨機并行
英文
random paralleling-
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。Genetic algorithm is a random searching method which simulates natural selection and evolution. this method has some advantages that other usual methods do n ' t have because of its two characters - - - - - - implicit parallelism and global searching
遺傳演算法是模仿自然選擇與進化的隨機搜索方法，由於其隱含并行性和全局搜索特性，使其具有其他常規優化演算法無法擁有的優點。Recently years, there is a new optimization method named genetic algorithms ( ga ) which is based on the numbers of genus groups. this method is a kind of random searching method which simulated natural selection and evolution. compared with traditional optimization method, genetic algorithms has two notable characters. one character is latent parallel and the other is seaching in the whole area. and genetic algorithms has some advantage which traditional method do n ' t have, for example, in genetic algorithms we did n ' t need the calculation of grade
遺傳演算法[ geneticalgorithms ，簡稱ga ]是近些年來出現的一種模仿自然選擇與進化的基於種群數目的隨機搜索演算法，是優化領域的一個新成員。與常規優化演算法相比，遺傳演算法具有隱含并行性和全局搜索特性這兩大顯著特徵，並具有一些常規優化演算法所無法擁有的優點，如不需梯度運算等。With the development of computer networks and computing science, paralleling computer and interconnection networks, covering mathematics 、 computing science 、 information science and so on, are becoming one of the hotspots of computer science research. all kinds of interconnection networks with different topologies, such as ring, mesh, hypercube, star topology network etc., have been received rapidly development
隨著計算機網路技術與計算科學的發展，并行計算機及其互連網路作為一個跨數學、計算科學與信息科學等多門學科的領域，逐漸成為計算機科學研究的熱點之一，各種拓撲結構的互連網路，如環、 mesh 、超立方體、星型網路等得到迅速發展。Along with the development of advanced manufacturing system, such as cims, ce, ims, vms, am and so on, the development of capp was further put forward whether on extent or on depth. recently, practicability, integration, intelligence and network will have being the development tendency of capp
隨著計算機集成製造系統（ cims ） 、并行工程（ ce ） 、智能製造系統（ ims ） 、虛擬製造系統（ vms ） 、敏捷製造系統（ am ）等先進製造系統的發展，無論從廣度上還是從深度上，都對capp的發展提出了更新更高的要求。Genetic algorithm ( ga ) is a kind of highly paralel, stochastic, global probability search algorithm based on the evolutionism such as natural selection, genetic crossover and gene mutation
遺傳演算法是一種基於自然選擇、遺傳雜效和基因變異等生物進化機制的高度并行、隨機、全局性概率搜索演算法。We show that our algorithm require only one scan, regardless of the database size, and during our algorithm working, there are no a good deal of frequent item - sets, which make our algorithm work excellent
本演算法採取「隨機并行搜索」策略，快速識別出候選關聯規則，整個挖掘過程最後只需掃描數據庫一遍，也不需生成大量的頻繁項目集，從而提高關聯規則挖掘的總體性能。With the advance of hpc, many high performance computers are developed and used
隨著高性能計算的發展，各種并行計算機不斷涌現並得到廣泛使用。This thesis suggests a process considered minimizes the population size as similar individuals occur in the fitter members of the population, which helps reduce the execution times for ga by removing the redundancy associated with the saturation effect found in the later generation. this thesis uses a method that adds dynamic penalty terms to the fitness function according to the optimal degree of solutions, so as to create a gradient toward a feasible suboptimal or even optimal solutions. on the basis of the difference of the biggest and the smallest of fitness of individual, modifying the fitness function in order to convergence is a satisfaction
動態調節種群大小，去掉遺傳演算法在迭代後期搜索產生的過多相似個體，達到減少計算時間的目的；按照解的優劣程度給適應度函數增加一個在ga搜索過程中動態改變的可變罰函數，給搜索最優解創造一個梯度，使遺傳演算法收斂到可行的較優解或最優解；根據適應度值最大和最小個體的差修正適應度函數，使適應度函數值適中不容易造成收斂太快、局部收斂或根本不收斂而變成隨機搜索；為了避免「近親繁殖」採用競爭擇優的交叉操作；利用并行遺傳演算法的思想，提出一種自適應多子種群進化策略；提出人口汰新政策來解決類似甚至相同的個體的情況發生。The mutual excitation between the local stimuli satisfying the rules of curve distribution ( position and orientation continuity ) called curve self - excitation is a useful method to discover and enhance curves and to inhibit noise. the present approaches used parallel connection structure division which did not acquire satifactory effect. this paper presents the idea of random time division and dynamic self - excitation, for different curves performing random time - division searches, time coincidence filtering, and self excitation accumulation. the principle is given
利用空間分佈滿足曲線規則（位置和定向連續性）的局部刺激之間的相互激勵，稱為曲線自激，這是發現視覺邊界曲線和抑制局部噪聲的有效手段.過去的工作均採用并行結構區分的計算方式，曲線自激並沒有達到滿意的效果.本文提出隨機時分動態自激的計算方案，對不同的曲線實施隨機時分的搜索、時間一致性濾波、和自激積累等機制.本文給出了實現的原理方案Genetic algorithm ( ga ) is a high - effective randomly searching algorithm, based on the nature evolution. it is a very effective algorithm to resolve np - completed combination optimization problem
遺傳演算法是一種借鑒于生物界自然選擇和進化機制發展起來的高度并行、自適應的隨機搜索演算法，是一種非常有效的解決np完全的組合問題的方法。With the development of computer technology and network technology, the distributed parallel computation environment that based on network become a new high performance calculation environment because of its high cost performance and big range, large quantity heterogeneous cluster system parallel computation
隨著計算機技術和網路技術的發展，以網路為基礎的分散式并行計算環境以其較高的性能價格比和大范圍、大數量異構機群并行成為新的高性能計算環境。At one time the thesis look back the part parallel interference cancellation detection, and update the algorithm of the multiuser with lms algorithm. at last, the thesis presentes the blind multiuser detection with adaptive algorithm the blind multiuser detection base on kalman algorithm and probabilistic algorithms for blind adaptive multiuser detection
同時對部分并行干擾多用戶檢測器進行了回顧，並用lms演算法實現了多用戶檢測器的演算法更新。最後對盲多用戶檢測的自適應演算法進行了介紹，構造基於kalman濾波的盲多用戶檢測器，並對隨機梯度演算法進行了誤碼性能的分析。Genetic algorithm is a kind of stochastic whole - searching regression algorithm, which is built on natural selection and molecule genetic mechanism, as a kind of universal algorithm to optimize the problems of complicated system, it is widely used in many fields due to its suppleness, universality, well self - fitness, robustness and fitness for collateral process, as a kind of bionic algorithms, the research on ga ' s application keeps far ahead of its theoretic research
遺傳演算法是藉助生物界自然選擇和遺傳學機理而建立的一種迭代全局優化隨機搜索演算法，是一種求解復雜系統優化問題的通用框架。它不依賴于問題的具體領域，具有簡單、通用、較強的自適應性和魯棒性，以及適于并行處理等顯著特點，因此被廣泛應用於眾多領域。作為一種仿生演算法，遺傳演算法的應用研究遠遠領先於演算法的基礎理論研究。He holds a phd in theoretical physics from the technical university of chemnitz, germany, where he investigated stochastic optimization algorithms on parallel computers
他從德國chemnitz技術大學獲得理論物理博士學位，在大學里他研究了并行計算機上的隨即最優化演算法。Combines the parallel character of multiple population pseudo parallel evolution with inner stochastic character of chaos movement, applying different chaotic disturbance strategies, taking chaos ' variable measure map mechanism into population initialization or middle populations fulfilled function optimization
把多群體偽并行進化的并行性和混沌運動的內在隨機性結合起來，利用不同的混沌擾動策略，把混沌變尺度映射機理應用到種群初始化和中間群體的優化進化實現函數優化。Three decoder architectures, parallel, serial and partially - parallel approaches, are analyzed in this thesis. a kind of novel partially - parallel architecture for decoding ldpc code is proposed. the trade - off between the performance of the decoder, hardware complexity and data throughout can be achieved with this partially - parallel architecture for the random parity check matrix
論文分析了三種不同的譯碼器結構:并行結構、串列結構以及部分并行結構，並提出了一種新穎的部分并行結構的ldpc譯碼器，較好地解決了當校驗矩陣為隨機結構時，譯碼性能、硬體資源和數據吞吐量平衡的問題。With the development of parallel processing technology and the extended application of parallel computers, parallel computers are now in great demand
隨著并行處理技術的不斷發展以及并行計算機的日益推廣應用，越來越多的人要求使用并行機。Genetic algorithm ( ga ) is a randomized parallel search algorithm that model natural selection, the process of evolution. ga has been widely used in engineering problems
遺傳演算法是一種模擬生物自然選擇、進化過程的隨機、并行搜索演算法，該演算法廣泛應用於解決工程技術問題。4 ) the circle data partition method of spectral adjoint model is put forward. the method increases 30. 7 % of parallel computing ratio under distributed - memory environments with 32 - processors. the butterfly - net data redistribution of spectral adjoint models is put forward
4 ）提出了譜離散伴隨模式并行計算的有效循環數據分配方法，採用該方法在某分散式存儲并行計算機上32臺處理機并行計算效率提高了30 . 7 。分享友人