爬山演算法 的英文怎麼說

中文拼音 [páshānyǎnsuànfǎ]
爬山演算法 英文
hill climbing
  • 爬 : Ⅰ動詞1. （爬行） crawl; creep 2. （抓著東西往上去; 攀登） climb; clamber; scramble Ⅱ名詞（姓氏） a surname
  • 山 : 名詞1 （地面形成的高聳的部分） hill; mountain 2 （形狀像山的東西） anything resembling a mountain...
  • 演 : 動詞1 （演變; 演化） develop; evolve 2 （發揮） deduce; elaborate 3 （依照程式練習或計算） drill;...
  • 算 : Ⅰ動詞1 （計算數目） calculate; reckon; compute; figure 2 （計算進去） include; count 3 （謀劃;計...
  • 法 : Ⅰ名詞1 （由國家制定或認可的行為規則的總稱） law 2 （方法; 方式） way; method; mode; means 3 （標...
  • 爬山 : climb mountains爬山方法 hill climbing method; 爬山郊遊會 climbing party; 爬山纜車 lift; 爬山賽 [體育] hill climb
  1. To deal with the question that how we can guarantee the dna sequences which are stored in the personal dna database are anonymous, that no one can find out whom a special dna sequence is collected from, this paper get a new method ? savior, by improve dnala ( dna lattice anonymization ), which is a method settling this question. savior replaces the multiple alignment in dnala with pairwise alignment between every tow sequences, and replaces the greedy algorithm in dnala with stochastic hill - climbing. for doing this, it can save the time for data pretreatment, and add the precision of classing

    針對個人dna數據的隱私保護問題，即:如何保證無法將存儲在數據庫中的dna序列信息與其提供者的個人身份信息（如:姓名，身份證號碼等）聯系起來，本文對一種新近開發的隱私保護方法? dnala （ dnalatticeanonymization ）進行了改進，在數據預處理階段，用兩兩雙序列比對代替了原演算法中的多序列比對，在不降低處理精度的情況下減少了數據預處理所耗費的時間;用隨機爬山法代替了原演算法中的貪心策略，增加了演算法後期處理的精度，從而形成了一種新的演算法? savior 。
  2. Considering all the bizarreness of higher dimensional hill - climbing problems ( and the neat algorithms that have been devised for their solution ), there could be some very interesting displays and control tools provided to the human team member

    考慮到更高級的爬山問題的棘手性質（以及為此設計的精妙演算法） ，可能會出現非常有趣的局面，包括為小組中人類成員提供的控制工具。
  3. In the process of seeking the inverse solution, the traditional method abandoned, a new " way - - - - ihca was gained by extracting the advantages of hill climbing and enlightening from the numeral described in the computer threory, and the algorithm flow chart was written out clearly

    在求取運動學逆解的過程中，對新方法進行努力的探索。吸收人工智慧中的啟發式搜索演算法? ?爬山法的優點，從計算機原理中位置數制的描述獲得啟發，得到搜索演算法的步長，探尋求取運動學逆解新方法? ?智能登山法，並寫出完整的演算法流程。
  4. Optimized mountain - climb searching in auto - focusing

    自動對焦中的優化爬山搜索演算法
  5. Iterative tfidf algorithm belongs to hill - climbing algorithm, it has the common problem of converging to local optimal value and sensitive to initial point

    迭代tfidf演算法屬于爬山演算法，初始值的選取對精度影響較大，演算法容易收斂到局部最優值。
  6. Enforced hill - climbing with backtracks can use the last searching results when enforced hill - climbing fails, so it is more fit for ff. helpful action ordering can save the time used by enforce hill - climbing through ordering the helpful actions

    帶有回溯的加強爬山演算法在加強爬山演算法失敗時，通過回溯使規劃器可以利用前一步的搜索結果來繼續進行加強爬山搜索。
  7. An axial direction line multi - sensors placed method is presented. in the process of machine learning, use genetic algorithm to fit the curve of sensor ' s output. to improve the convergence speed of genetic algorithm, hill climbing method is joined with

    對示教過程中進行曲線擬和所用的遺傳演算法進行了一定的研究，為了解決遺傳演算法在接近收斂點時收斂速度慢的缺點，使用爬山法對其進行改進。
  8. In the stochastic hill - climbing, the number of climbing is an important parameter, and is different to get a good value. this paper use experimental technique to settle this question. it use the least square method find a polynomial empirical formula, which can compute the number of climbing

    另外，針對savior演算法中隨機爬山法爬山次數難以確定的問題，本文採用多項式最小二乘擬合，利用實驗方法得到了問題規模和隨機爬山法合理爬山次數間的經驗公式。
  9. The reason why we integrate them is that k - means algorithm is a mountain climbing method, which is easy convergent to local extremum, and sensitive to the original condition, but its convergent speed is relatively fast, and that genetic algorithm is a random searching method, which can find the whole extremum in a rather big probability, and non - sensitive to the original condition, but its convergent speed relatively slow

    之所以將：二者結合在一起，是回為k一均值演算法是一種爬山法，容易收斂到岡部極小值，對初始條件較敏感，但收斂速度較快，而遺傳演算法是卞dl隨機搜索演算法，能夠以較大概率找到全局最憂解，且對們始條件個敏感，但收斂速度較慢。
  10. Conventional clustering criteria - based algorithms is a kind of local search method by using iterative mountain climbing technique to find optimization solution, which has two severe defects - sensitive to initial data and easy as can get into local minimum

    傳統的基於聚類準則的聚類演算法本質上是一種局部搜索演算法，它們採用了一種迭代的爬山技術來尋找最優解，存在著對初始化敏感和容易陷入局部極小的致命缺點。
  11. Research of maximum power extraction algorithm for inverter - based variable speed wind turbine system is going on in the last chapter. main maximum power extraction algorithm is discussed and compared. a new mppt control algoririthm is discussed to get fast and stable traking of maximum wind power in detail, then advanced hill - climb searching method has been developed for maximum wind power traction

    因此本文最後對一些最大風能俘獲的先進演算法進行了研究，仔細探討了最大功率點跟蹤演算法，並提出了一種先進爬山搜索風力發電最大功率俘獲智能演算法，採用該演算法不需測量風速和風機轉子速度，並且與系統特徵參數獨立，能應用於大小各種風機，具有非常好的效果。
  12. This algorithm makes use of these characteristics and is improved according to the practical characteristic of communication system. not only the early converge problem of genetic algorithm is avoid, but also the optimize solution in the colony is reserved. meanwhile mountain climbing performance of simulated annealing algorithm is used to improve the performance of genetic algorithm

    該演算法利用遺傳方法適于多變量數值求解、有較好的兼容性及模擬退火演算法有很好的加速性，結合通信系統的實際特點進行改進，不但避免了遺傳演算法的早熟收斂問題，同時使群體中的最優解得到了保留，並利用模擬退火演算法的爬山性能改善了遺傳演算法的性能。
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