問題搜尋 的英文怎麼說

中文拼音 [wèntísōuxún]
問題搜尋 英文
problemistic search
  • 問 : Ⅰ動詞1 （請人解答） ask; inquire 2 （詢問; 慰問） question; ask about [after]; inquire about [aft...
  • 題 : Ⅰ名詞1. （題目） subject; title; topic; problem 2. （姓氏） a surname Ⅱ動詞（寫上） inscribe; write
  • 搜 : 動詞1. （尋找） collect; gather2. （搜查） search; ransack
  • 問題 : 1 （需回答的題目） question; problem 2 （需研究解決的矛盾等） problem; matter 3 （事故或意外） tr...
  • 搜尋 : search for; look for; seek
  1. Performance problems come in many guises, giving you ample opportunity to indulge your clue - hunting proclivities to identify and resolve them

    性能問題以多種形式出現，為您提供了縱容自己的線索搜尋癖好來確定和解決這些問題的大量機會。
  2. Unification of chinese terms in psychology

    上崗引導過程中信息搜尋問題的研究進展
  3. One is the bss based on kernel density estimation ( kde ) and genetic algorithm ( ga ), the other is the blind deconvolution based on high order cross cumulants and ga. without nlf, the performance of separation in both algorithms is independent with the kurtosis of the sources

    兩種演算法的實現無需引入非線性函數，因此都與源信號的峭度性質無關;另外，選取全局搜索的遺傳演算法進行尋優，避免了梯度法搜索的局部性，使得演算法均能收斂到問題的全局最優解。
  4. With applying dsst algorithm, mobile agent can solve the problem of selecting router. synthesizing advantages of centralized search engine and distributed search engine, through it that system obtains the best way that agents mobile does. after statistics analysis, the author gets the mathematic pattern that mobil agent clones right times

    利用idl語言定義了agent在多種平臺上移動的介面，設計了一個符合maf規范的插件，實現agent在異種平臺之間的移動；利用動態最小生成樹演算法解決移動agent的路由選擇問題，綜合集中式搜索和分散式搜索的特點，動態尋找出一條agent移動的最佳路線；通過統計分析得到移動agent克隆的數學模型，通過計算可以獲得agent克隆個數的最佳值。
  5. Because ga possesses the traits of can global random search, the robustness is strong, been use briefly and broadly, it didn ’ t use path search, and use probability search, didn ’ t care inherence rule of problem itself, can search the global optimum points effectively and rapidly in great vector space of complicated, many peak values, cannot differentiable. so it can offset the shortages of nn study algorithm, can reduce the possibility that the minimum value get into local greatly, the speed of convergence can improve, interpolation time shorten greatly, the quantity of training reduce

    因為遺傳演算法具有全局隨機搜索能力，魯棒性強、使用簡單和廣泛的特點，它不採用路徑搜索，而採用概率搜索，不用關心問題本身的內在規律，能夠在復雜的、多峰值的、不可微的大矢量空間中迅速有效地尋找到全局最優解，所以可以彌補神經網路學習演算法的不足，使陷入局部最小值的可能性大大減少，使得收斂速度提高，訓練量減小。
  6. 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。
  7. In this article, we use idea of turning dispersion into convergence and put all the well ' s points into the same unit net to think about it. and answer three questions of the distribution of well drilling by the way of searching for groups of points. fincite - step - searching underthe condition of translationg fcoordinate system or revolving coordinate system. to first question. we find two algo - rithms and make use of data that is given to find the solution. we seek coorlinate of net point is co. 361, 0. 461 ) and mostly four old well ' s points are utilized at the same time by first algorithim, which are no. 2, no, 4, no. 5, no. 10. by second algorithm, we rechon the coordinate of net point is co. 390, 0. 505 ). and that mostly four old well ' s points are utilizld which are no. 2, no. 4, no. 10. to second question, we turn it into the first question by angling awt the center of net point. we seek that mostly six old well ' s points are utilized at the same time, which are no. 1, no. 6, no. 7, no. 8, no. 9, no. 11, when net is angled 0. 78 radian. and net point is translated to ( 0. 75, 0. 076 ) ( at nwe coordinate system ). to third question, wefind a necessary and sufficient condition and affer algorithms, at last, we analyse algorithms

    運用化分散為集中的思想，把所有的井點都放在同一個單位網格內考慮.在坐標可平移、旋轉的條件下，利用尋找點群、有限步驟搜索法，對鉆井布局的三個問題進行了解答.對問題一，給出了兩個不同演算法.並對題目提供的數據進行了求解，演算法1得到的結點為（ 0 . 361 ， 0 . 461 ） ，最多有4個舊井點被同時利用，它們是第2 、 4 、 5 、 10個井點;演算法2得到的結點為（ 0 . 390 ， 0 . 505 ） ，最多有4個井點被利用，它們是第2 、 4 、 5 、 10個井點.對問題二，以結點為中心旋轉一定的角度后，歸結為問題一進行求解，求解結果為當網格傾斜角為0 . 78弧度（相對原坐標系） ，結點平移到（ 0 . 75 ， 0 . 076 ）點（在新坐標系下） ，可被同時利用的最多舊井點為6個，它們是第1 、 6 、 7 、 8 、 9 、 11個井點，對問題三，我們給出了充要條件，並給出了演算法.最後還分析了演算法的優劣性
  8. The proposed algorithm can not only predigests the seeking process, but also finds the available solution of original problem automatically

    在大大簡化求解過程的同時，有效地保證了自動搜尋出原問題的有效解。
  9. Late in the process, just prior to the purchase decision, the searcher now has a thorough understanding of specific needs, wants, and issues affecting the purchase decision

    后來的過程，在形成購買決定之前，搜尋者現在充分了解了具體需要，需要和影響購買決定的問題。
  10. In chapter four, problem about parameters of gpc on - line tuning in frame of satisfactory control is researched, and a satisfactory model for multi - parameters on - line tuning is defined, and proposed a formula based on grade conception and one - dimension searching method. it ' s shown that the system ' s performance can be improved by this approach

    對多參數調整優化問題給出了滿意優化模型，並且對由此形成的多變量尋優問題引入了梯度的概念和一維搜索的方法給出了該多參數尋優問題求解迭代公式的杜十模糊btai與模糊約柬的浦意優化控制向量形式。
  11. This article is a stage achievement of an on - going research project entitled " management consulting in china : a strategy for development " and funded by the natural science foundation of china ( nsfc ) c in applying the main theoretical guidelines of the project, the author empirically investigated and evaluated, with a comparative perspective and through measures of literature survey, interview and case study, the scientifec knowledge base and knowledge acquiring system of the management consulting profession under the chinese context, with main focuses being put on 4 dimensions : the existing literature on consulting and management consulting, training programs offered by professional organizations, programs of on - the - post training, and the university - based education. on this basis, the author summarizes several existing problems and constraints and relates them to the strategies of developing management consulting in china

    作者在遵循和應用項目主要理論主線基礎上，採用實證兼中外比較研究的方法，通過文獻調查、網路搜尋、訪談（直接和電話訪談） 、案例分析等手段，從存在之科學文獻、管理咨詢專業組織（協會）與管理咨詢公司提供的在崗訓練、高校提供的教育與培訓這四條途徑，實證考查我國管理咨詢專業科學知識體系及知識獲取系統發育狀況，從而發現存在問題，並希翼在此基礎上發展出相關之發展戰略。
  12. The novel optimization algorithm, genetic algorithm popular these years, is introduced. genetic algorithm has the characteristics of multi - point searching, parallel computing and self - adaptive global optimization. so it is very suitable for the solution of complex engineering problems, which often have numerous variables, high dimension, highly nonlinear optimization objects, and the great solution spaces

    引入了近幾年興起的新型優化演算法? ?遺傳演算法，其具有多點搜索、并行計算和自適應全局尋優的特點，特別適用於求解離散型設計變量多、維數高、優化目標高度非線性、解空間十分龐大的復雜工程問題。
  13. Qga combining the genetic algorithm and the quantum information theory has a large search space with small population and a good global search capability, while image sparse decomposition based on mp is an optimal problem, so it can be fast solved by qga

    量子遺傳演算法能用較小的種群規模實現較大的空間搜索，全局尋優能力強，基於匹配追蹤的圖像稀疏分解是最優化問題，因此可用量子遺傳演算法快速實現。
  14. They conversed with one another while searching, asking questions and giving advice

    他們搜尋的時候會和別人交談，並且詢問問題和給建議。
  15. Though simulating the state of packing, adopting the experience of manual packing and heuristics knowledge to control the searching way and limit the search space, it changes the packing problem into the problem of searching best path in the state space

    文中對多邊形的合成技術進行了研究，通過模擬布局狀態，吸取人工布局的經驗，採用啟發式知識控制搜索方向，限制搜索空間，把布局問題轉化為在狀態空間下尋找最優路徑的問題。
  16. In this paper, a new interface - searching algorithm - boundary cell method ( bcm ) based on the fact that interface happens on the boundary surface is presented particularly

    在接觸問題中，接觸搜尋是極其重要的。為此，本文提出了一種新的接觸搜尋法? ?邊界子域法（ bcm ） 。
  17. Obviously, the artificial ground motions used for structural time history analysis should well coincide with multi - damping - ratio - spectra. based on the comparison of the conventional algorithms for multi - objective optimization and genetic algorithms, a method for the simulation of multi - damping - ratio - spectra is proposed in the thesis which combines the multi - objective optimization algorithms and genetic algorithms. the program corresponding to the method is also developed and the method is proved to be feasible and convenient by an example at the end of the thesis

    本文通過分析多目標優化問題的解的意義和常用解法，在比較現有的幾種常用優化演算法的基礎上，選用遺傳演算法這種自適應全局優化概率搜尋演算法作為本文的搜尋方法，將其與多目標優化理論有機地結合在一起，形成多目標遺傳演算法，並將其引入地震動模擬，形成基於遺傳演算法擬合多阻尼比反應譜的地震動模擬程序。
  18. Based on tabu search, a local search technique with changeable local structure ( algorithm 2 ) is proposed for this np - hard problem, which provides an approach to control the optimizing process flexibly

    基於tabu搜索設計了變鄰域結構的局部搜索技術，提供了靈活控制尋優過程的途徑;全多項式時間復雜性近似策略的開發，使得0一1背包問題的求解成為了n戶c問題近似演算法設計中最為成功的範例。
  19. It is widely applied to the domain of combinational evolutionary problem seeking, self - adapt controlling, planning devising, machine learning and artificial life etc. however, there are multi - objective attributes in real - world optimization problems that always conflict, so the multi - objective genetic algorithm ( moga ) is put forward. moga can deal simultaneously with many objections, and find gradually pareto - optimal solutions

    由於現實世界中存在的問題往往呈現為多目標屬性，而且需要優化的多個目標之間又是相互沖突的，從而多目標遺傳演算法應運而生，它使得進化群體并行搜尋多個目標，並逐漸找到問題的最優解。
  20. Just like other optimization problems, coalition formation need search the optimal solution or quasi - optimal solution in a complex and huge space. if algorithm traverses the whole search space, search process will lead to combination explosion that make it impossible for algorithm to complete search in polynomial time

    與其它的最優化問題一樣，聯盟形成需要在復雜而龐大的搜索空間尋找最優解或滿意解，如果遍歷整個搜索空間，就會產生搜索組合爆炸，在多項式時間內無法完成搜索。
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