適應遺傳信息 的英文怎麼說

中文拼音 [shìyīngyízhuànxìnxī]
適應遺傳信息 英文
adaptive genetic information
  • 適 : 形容詞1 （適合） fit; suitable; proper 2 （恰好） right; opportune 3 （舒服） comfortable; well Ⅱ...
  • 應 : 應動詞1 （回答） answer; respond to; echo 2 （滿足要求） comply with; grant 3 （順應; 適應） suit...
  • 遺 : 遺動詞[書面語] （贈與） offer as a gift; make a present of sth : 遺之千金 present sb with a gener...
  • 傳 : 傳名詞1 （解釋經文的著作） commentaries on classics 2 （傳記） biography 3 （敘述歷史故事的作品）...
  • 息 : Ⅰ名詞1 （呼吸時進出的氣） breath 2 （消息） news 3 （利錢; 利息） interest 4 [書面語] （子女） on...
  • 適應 : suit; adapt; get with it; fit
  • 遺傳 : [生物學] heredity; hereditary; inheritance; inherit
  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. We use different genetic teaching methods to promote student ' s practice of making experiments and utilization of internet resources ; stimulate and direct student ' s desire of exploring the knowledge properly ; impel students to think and analyse deligently ; enhance their ability of thinking, utilizing and operating ; foster student ' s ability to grasp information and self - study ability ; increase student ' s comprehensive ability through all directions to meet the needs of the quick development of life science in 21st century

    摘要通過在遺傳學教學中採用不同的教學方式、加強實驗操作和運用網路資源的實踐，合理引導並激發學生對知識探索的慾望，促使學生勤於思考與分析，提高思維能力、運用能力和動手能力，培養學生具有信息能力和自主學習能力，全方位多角度地提高學生的綜合素質，以適應21世紀生命科學迅速發展的需要。
  3. Firstly, influence factors of generalization of neural network are presented in this thesis, in order to improve neural network ’ s generalization ability and dynamic knowledge acquirement adaptive ability, a structure auto - adaptive neural network new model based on genetic algorithm is proposed to optimize structure parameter of nn including hidden layer nodes, training epochs, initial weights, and so on ; secondly, through establishing integrating neural network and introducing data fusion technique, the integrality and precision of acquired knowledge is greatly improved. then aiming at the incompleteness and uncertainty problem consisting in the process of knowledge acquirement, knowledge acquirement method based on rough sets is explored to fulfill the rule extraction for intelligent diagnosis expert system, by completing missing value data and eliminating unnecessary attributes, discretization of continuous attribute, reducing redundancy, extracting rules in this thesis. finally, rough sets theory and neural network are combined to form rnn ( rough neural network ) model for acquiring knowledge, in which rough sets theory is employed to carry out some preprocessing and neural network is acted as one role of dynamic knowledge acquirement, and rnn can improve the speed and quality of knowledge acquirement greatly

    本文首先討論了影響神經網路的泛化能力的因素，提出了一種新的結構自適應神經網路學習演算法，在新方法中，採用了遺傳演算法對神經網路的結構參數（隱層節點數、訓練精度、初始權值）進行優化，大大提高了神經網路的泛化能力和知識動態獲取自適應能力;其次，構造集成神經網路，引入數據融合演算法，實現了基於集成神經網路的融合診斷，有效地提高了知識獲取的全面性、完善性及精度;然後，針對知識獲取過程中所存在的不確定性、不完備性等問題，探討了運用粗糙集理論的知識獲取方法，通過缺損數據補齊、連續數據的離散、沖突消除、冗餘信息約簡、知識規則抽取等一系列的演算法實現了智能診斷的知識規則獲取;最後，將粗糙集理論與神經網路相結合，研究了粗糙集-神經網路的知識獲取方法。
  4. The image features of object and ambiance in the real robot visual servo system are analyzed. the method of object recognition based on image segmentation and corner detection is presented. an image is segmented into several regions by image segmentation

    分析了實際的機器人視覺系統中目標物體以及環境的圖像特徵，提出基於圖像分割與角點檢測的物體識別方法，通過圖像分割將整幅圖像分割為一個個的單一物體區域，分別利用角點檢測，找出與模板角點信息相匹配的區域，然後利用基於模糊自適應操作運算元遺傳演算法的圖像匹配方法尋求最優匹配。
  5. Perfection and adjustment according to system properties, it combines genetic algorithms with fuzzy control, detailed analyzes the problem of designing fuzzy controller and proposes two advanced schemes : first scheme : the change - of - variables are emerged into input variables of the simple fuzzy controllers of oil feeding pump system as one variable, and one pi block is connected after output of fuzzy controllers, consequently the structure of the improved fuzzy controller is analyzed, finally genetic algorithms with adaptive probabilities of crossover and mutation is applied to optimize membership functions and fusing factors of the fuzzy controllers, and the simulation results of before and after optimization are compared

    由於在模糊控制器的設計過程中存在較多的人為因素，為了實現根據系統特性對模糊規則和隸屬函數進行自動修正、完善和調整，本文將遺傳演算法和模糊控制結合起來，並針對前面設計的模糊控制器中所存在的問題進行了詳細分析，提出了兩種改進方案： 1在簡單模糊控制器的輸入變量中加入了變量變化率的信息，即將輸入變量和變量的變化率融合為一個輸入量，並在模糊控制器的輸出端加入比例、積分環節，然後分析了這種改進后的模糊控制器的解析結構，最後採用改進后的自適應遺傳運算元的遺傳演算法對模糊控制器中的隸屬函數和融合因子進行優化，並將優化前後的結果作了比較和分析。 2
  6. For standard genetic algorithm has the defects of slowly converging and easily falling into local extremum ， the author designed and realized the adaptive multi - population parallel genetic algorithm ( ampga ) to solve the reliability allocation problem of large and complicated software systems. finally, we experimented on the comity center subsystem, delivery center subsystem and system management subsystem of the project : the jiangsu province postal logistics information system

    針對標準遺傳演算法存在著收斂速度慢、易陷入局部極小值等缺點，本文設計並實現了自適應多種群并行遺傳演算法（ ampga ） ，來解決大型、復雜軟體系統的可靠性分配問題。最後，對「江蘇省郵政物流信息系統開發」項目中的「禮儀中心子系統」 、 「遞送中心子系統」及「系統管理子系統」進行了可靠性分配實驗。
  7. An optimizing method based on biological evolution - genetic algorithm is proposed to the problem of the construction process planning analysis. adapting the successful experience of path optimization problem, improved genetic operators are adopted : the sequence of numbers, which is arranged by the removal order of members, is assumed to represent a chromosome by natural code ; transforming baseline information of this problem into immune operators ; combining rank - based model with elitist model ; designing special crossover and mutation operators. the mathematical model for computer and special programs are provided in this paper

    借鑒遺傳演算法在求解路徑優化問題上的成功經驗，應用了改進的遺傳運算元：採用自然數對結構去除構件進行編碼，將所求問題的基本信息轉換成免疫運算元，採用穩態最優保存策略和排序選擇方法相結合進行選擇，採用適用於本問題研究的特殊的交叉運算元和變異運算元，從而來討論在桁架結構施工路徑優化問題中使用遺傳演算法的可能性。
  8. Genetic algorithm ( ga ), developed by american professor j. holland, is a sort of randomly searching algorithms which originated from the creature rule - nature selection and genetic mechanism, and has a main characteristic which is that the group searching strategy and the switching and searching for message between individuals are independent of gradient information

    遺傳演算法由美國教授j . holland提出，它是一類起源於生物選擇和遺傳機制的隨機搜索演算法。遺傳演算法的主要特點是群搜索策略以及個體間信息交換和搜索不依賴梯度信息，它特別適合於傳統方法難于解決的復雜和非線性問題並廣泛應用於機器學習、自適應控制、組合設計、人工智慧等領域。
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