粒子群集 的英文怎麼說
中文拼音 [lìziqúnjí]
粒子群集
英文
swarm of particles-
Of the eons of geological periods recorded in the stratifications of the earth : of the myriad minute entomological organic existences concealed in cavities of the earth, beneath removable stones, in hives and mounds, of microbes, germs, bacteria, bacilli, spermatozoa : of the incalculable trillions of billions of millions of imperceptible molecules contained by cohesion of molecular affinity in a single pinhead : of the universe of human serum constellated with red and white bodies, themselves universes of void space constellated with other bodies, each, in continuity, its universe of divisible component bodies of which each was again divisible in divisions of redivisible component bodies, dividends and divisors ever diminishing without actual division till, if the progress were carried far enough, nought nowhere was never reached
隱藏在大地的洞穴里和能移動的石頭底下蜂巢和土墩子中那無數微小的昆蟲類的有機生物：微生物病菌細菌桿菌精子憑著分子的親和之凝聚力而粘在一根針尖上那幾萬幾億幾兆個多不勝數肉眼看不到的微小顆粒人類的血漿是一個宇宙，群集著白血球和紅血球，每個血球又各自形成一個空虛的宇宙空間，群集著其他球體各個球體連續性地也是由可分割的構成體形成的宇宙，各個構成體又可以分割成為幾個能夠進一步分割的構成體。就這樣，分子與分母實際上在並未分割的情況下就不斷地減少了。如果這個過程延續到一定時候，就永遠在任何地方也不會達到零。The experimental results indicate that it is easy to be realized, can save the calculating cost and improve the constringency speed
試驗結果表明，用粒子群演算法來訓練樣本集具有容易實現、節省計算成本和提高收斂速度等優點。Pso idea is an optimization technique inspired by swarm intelligence and theory in general such as bird flocking, fish schooling and even human sacial behavior
粒子群優化演算法（ particleswarmoptimization簡稱pso ）是一種基於群智能的演化計算技術，源於對鳥群覓食過程中的遷徙和聚集的模擬。In order to add the resource collected by nerms resource collecting system to database according to its content, we must give a label for each resource. if we just do the work one by one manually, we will find it ' s a tedious job and we must find a new tool to help us, so the resource classifier l
本文提出一個頻率統計函數，一個基於頻率的分類規則適應度函數以及一個用於分類的打分函數，並把這三個函數結合適用於粒子群演算法的分類規則編碼來更準確的提取規則集，然後通過修改粒子位置更新方程使粒子群演算法適于解決分類規則挖掘問題。Pso is simple and efficient, so many researchers have been attracted by this algorithm, and furthermore, it converges fast by moving each particle aimed at guides when it deals with single - objective optimization, and these features are important in multi - objective optimization also. from some current research works, we describe a multi - objective particle swarm optimization algorithm ( mopso ) that incorporates the concept of the enhanced - dominance, we present this new concept to update the archive, the archiving technique can help us to maintain a sequence of well - spread solutions. a new particle update strategy and the mutation operator are shown to speed up convergence
目前，國內外已有部分相關研究成果，但是它們在解集分佈性、收斂性方面仍存在不足，在吸取已有成果的基礎上，本文提出了一種改進的多目標粒子群演算法（ mopso ） ，使用我們提出的強支配概念構造外部種群，使解集保持良好的分佈性，同時，通過採用新的全局極值和個體極值的選取方式及採用新的種群更新策略加快解集的收斂，提出基於快速排序的非支配集構造方法加快演算法運行效率。To avoid the possible occurring of stagnation phenomenon in the particle swarm optimization algorithm, the re - initialization mechanism based on the global information feedback is introduced in the hpso
針對粒子群優化演算法可能出現的停滯現象， hpso引入11強跟蹤狀態估計與群集辨識浙江大學博士學位論文了基於全局信息反饋的重新初始化機制。The mrci program based on hole - particle symmetry has been parallelized and tested on a ia cluster consisting of 12 nodes
設計了基於空穴-粒子對稱的并行化組態相互作用程序， ?在一個有12個結點的計算機集群上測試了程序的運行。分享友人