隱含優先度 的英文怎麼說

中文拼音 [yǐnhányōuxiāndù]
隱含優先度 英文
implicit precedence
  • 隱 : Ⅰ動詞（隱瞞; 隱藏） hide; conceal Ⅱ形容詞1 （隱藏不露） hidden from view; concealed 2 （潛伏的; ...
  • 含 : 動詞1 （東西放在嘴裏 不咽下也不吐出） keep in the mouth 2 （藏在裏面; 包含） contain 3 （帶有某種...
  • 先 : 名詞1 （時間或次序在前的） earlier; before; first; in advance 2 （祖先; 上代） elder generation; ...
  • 度 : 度動詞[書面語] （推測; 估計） surmise; estimate
  • 隱含 : implication
  1. 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。
  2. The algorithm included three steps : firstly, the text sub - region was selected adaptively according to the feature that the edges contained in text regions was stronger than those in non - text regions ; secondly, the blank bars between two text lines were extracted by blank blocks searching ; thirdly, the skew angle of blank bar was calculated by directional fitting, and this skew angle was just the document skew angle

    該方法首先通過對梯度圖像的統計分析，自適應地選取到了包含文字的特徵子區；在特徵子區內，論文把文字行間的空白條帶看作一條隱含的線，用優化理論計算出空白條帶的傾斜角度，這也就是文本的傾斜角度。
  3. The notion of explicitly geometric shape feature position of primitive is introduced, and the classification of explicitly geometric shape feature position is studied carefully. secondly, taken the combining principle of combined entity as the basis, the notion of explicitly geometric shape feature position constraint of combined entity is introduced. the classification - the explicitly reductive capacity to location dimension, constraint degree, dividing constraint level of explicitly geometric shape feature position constraint are systematically studied

    其次，對組合體的組合機理進行了分析，並在此基礎上，引入了組合體的隱式幾何形狀特徵位的概念，系統的討論了組合體的隱式幾何形狀特徵位約束的分類、組合體的隱式幾何形狀特徵位約束對定位尺寸隱含性縮減的性質、組合體的隱式幾何形狀特徵位約束的約束度、隱式幾何形狀特徵位約束的優先級別劃分。
  4. 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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