隱含優化 的英文怎麼說

中文拼音 [yǐnhányōuhuà]
隱含優化 英文
hidden optimization
  • 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...
  • 隱含 : implication
  1. Firstly, second harmonic component ratio and dead angles of two phase inrush ' s dispersion in three - phase transformes are acted as input variable. secondly, the method applies improved algorithm based on the original algorithm of multi - layer forward back propagation network, that is to say, adding last variational effect of weight value and bias value to this time and making use of variable learning rate. at the same time, this method also adopts dynamic form in the number of hidden floor node

    首先，文中將三相變壓器兩相涌流差流的二次諧波含量比和間斷角作為網路的輸入變量；其次，利用對原有bp網路訓練演算法基礎上的改進型演算法（即在計算本次權值和閾值的變化時增加上一次權值和閾值變化的影響以及採用變學習率，與此同時隱含層神經元個數採用動態形式） ，通過樣本訓練使網路結構模型達到最優。
  2. By analyzing the puissance, experience, body and happiness language, i find that the really meaning of the female fashionable periodical is a kind of contradictive expression. it is a sign of status, but also is a reversed discrimination ; it wants be out of the traditional culture, but also in it ; elegant feminine temper is also in the charge of the masculine society ; happiness is based on the expenditure of body and substance. now the contradictive expression of feminine language is becoming the most important problem in the development of the female fashionable periodical

    筆者通過對女性時尚期刊權力、經驗、身體、快樂話語的解構和分析，發現女性時尚期刊的表達陷入了一定的話語矛盾和困境:是身份認同的標志，卻又隱含著反向歧視;經驗話語的「反文化」表達，實質上是建立在「泛文化」基礎上對傳統文化的部分回歸;優雅可人的女性氣質，說到底卻帶著男性規訓的深深烙印;快樂話語的傳播，是以對消費主義的追隨和女性身體的消費為前提的。
  3. 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。
  4. Thirdly, genetic algorithm is a kind of search and optimization method simulating the life evolution mechanism, which has the advantages of global optimization and implicit concurrency

    遺傳演算法是一種模擬生命進化機制的搜索和優化方法，與常規優化演算法相比，具有隱含并行性和全局搜索特性，因此選擇遺傳演算法進行尋優計算。
  5. 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。
  6. 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。
  7. 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。
  8. Ga is a computational models of the human evolution, with implicit parallelism and capacity of using effectively global information

    遺傳演算法（ ga ）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能力。
  9. Compared with traditional optimization methods, genetic algorithm has two notable characters. one is the latent parallelism and the other is searching in the whole area. genetic algorithm has some advantages which traditional methods do n ' t have

    與常規的優化演算法相比，遺傳演算法具有隱含并行性和全局收斂性兩大顯著特徵，並且具有常規優化方法所沒有的優點，如不需要梯度計算等。
  10. Genetic algorithms are search algorithms based on the mechanics of natural selection and natural genetics. it is widely used in many kinds of fields because of its less - dependency of optimization problem, simplicity, robustness and implicit parallelism

    遺傳演算法是模擬遺傳學和自然選擇機理構造的一種搜索演算法，因其對優化問題的弱依賴性、求解的簡單性和魯棒性、隱含并行性等特點被廣泛應用於當前的各個領域。
  11. Genetic algorithm is an fresh subject in recent years, it is a search algorithm based on the mechanics of natural selection and natural genetics. it is widely used in many kinds of fields because of its less - dependency of optimization problem, simplicity robustness and implicit parallelism

    遺傳演算法是近年來新興的一門學科，是模擬遺傳學和自然選擇機理構造的一種搜索演算法，因其對優化問題的弱依賴性、求解的非線性和魯棒性、隱含并行性等特點被廣泛應用於當前的各個領域。
  12. Bp model can quite improve the accuracy of pricing result ; 2. need to confirm the number of the hidden layer of neuron rationally ; 3. should confirm population size rationally while optimizing ann ; 4

    基本結論如下： （ 1 ） bp模型能夠提高定價結果的準確度；武漢理工大學碩士學位論文（ 2 ）建模需要合理確定隱含層神經元數目; （ 3 ）優化網路應恰當確定初始群體規模; （ 4 ）優化網路需要合理設計交叉運算元。
  13. In training of back - propagation neural network, parameter adaptable method which can automatically adjust learning rate and inertia factor is employed in order to avoiding systemic error immersed in a local minimum and accelerating the network ' s convergence ; introduced the further optimization of the network ' s structure, it gives the research result of selection of the hidden layers, neurons, and the strategy of re - learning, compared the sums of the deviation square of this algorithm with conventional bp algorithm, as a result, the approach accuracy and the generalization ability of the network were extremely improved

    在對前饋神經網路的訓練中，使用參數自適應方法實現了學習率、慣性因子的自我調節，以避免系統誤差陷入局部最小，加快網路的收斂速度；提出了優化bp網路結構的實驗研究方法，並給出了有關隱含層數和節點數選擇以及再學習策略引進的研究結果。將該演算法同傳統bp演算法的預測偏差平方和進行比較，結果證實網路的逼近精度及泛化能力均得到了極大的提高和改善。
  14. Section 3 is devoted to the stablities for the perturbed generalized equations. with some constraint qualifications, the pseudo - lipschitz continuities for solution mappings of generalized equations at the solutions is obtained, which implicits the pseudo - upper - lipschitz continuity at the same points. in the last, we derive necessary optimal conditions for optimal problems with quasi - variational inequalities

    第三節主要是對廣義方程的擾動穩定性進行研究，得到了在假設條件下，廣義方程解映像的局部lipschitz連續性質，它隱含了廣義方程解映像是偽上lipschitz連續的；最後，我們得到了具擬變分不等式約束的優化問題解的必要最優條件
  15. Clustering is one of the most important areas in data mining clustering finds the similarity among the data and use it to optimal the query of the large scale databases and find the hidden useful information and knowledge

    聚類分析是數據挖掘中的一個重要研究領域，它從數據庫中尋找數據間的相似性，從而優化大規模數據庫的查詢和發現數據中隱含的有用信息或知識。
  16. The use of full optimization implies the use of the frame pointer omission

    使用「完全優化」隱含使用「幀指針省略」 （
  17. The technique of optimizing is a important part of the system, if the system is not optimized, then there must be some defaults in the system which can make the system run inefficiently

    優化技術在一個系統開發的過程中具有相當重要的地位，沒有優化的系統常常會存在許多隱含的問題，使整個系統的運行效率降低。
  18. Control systems in modern automatic engineering are nonlinear, time - changed and indefinite. lt is difficult to model by traditional method, even sometime impossible. under these circumstances we should apply model identification to gain the approximate model of object for effective control, there are many models to be chosen, fuzzy model is one of them, it is put forward with the development of fuzzy control. fuzzy model has characteristics of general approximation and strong nonlinear, it is fit for describing complex, nonlinear systems. to avoid rules expansion when the number of input values are very big. in this paper we apply hierarchical fuzzy model to resolve this problem, we also illustrate it has general approximation to any nonlinear systems. genetic algorithm is a algorithm to help find the best parameters of process. lt has abilities of global optimizing and implicit parallel, it can be generally used for all applications. in our paper we use fuzzy model as predictive model and apply ga to identify fuzzy model ( including hierarchical fuzzy model ), we made experiments to nonlinear predictive systems and got very good results. the paper contains chapters as below : chapter 1 preface

    現代控制工程中的系統多表現為非線性、時變和不確定性，採用傳統的建模方法比較困難，或者根本無法實現，在這種情況下，要實現有效的控制，必須採用模型辨識的方法來獲取對象的近似模型，並加以控制，目前用於系統辨識的模型種類很多，模糊模型是其中的一種，它隨著模糊控制的發展而被人提出，模糊模型具有萬能逼近和強非線性的特點，比較適合於描述復雜非線性系統，為了解決模糊模型在輸入變量較多時規則數膨脹的問題，文中引入遞階型模糊模型，並引證這種結構的通用逼近特性。遺傳演算法是模擬自然界生物進化「優勝劣汰」原理的一種參數尋優演算法，它具有隱含并行性和全局最優化的能力，並且對尋優對象的要求比較低，在工程應用和科學研究中，得到了廣泛的應用，本文將遺傳演算法引入模糊模型的辨識，取得了很好的效果。
  19. 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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