遺傳學習 的英文怎麼說

中文拼音 [yízhuànxuéxí]
遺傳學習 英文
genetic learning
  • 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）...
  • 學 : Ⅰ動詞1 （學習） study; learn 2 （模仿） imitate; mimic Ⅱ名詞1 （學問） learning; knowledge 2 （學...
  • 遺傳 : [生物學] heredity; hereditary; inheritance; inherit
  1. It is obvious that a characteristic has been inherited in diseases such as haemophilia and traits such as naturally red hair : such things cannot be moulded by experience

    很明顯，有些病徵是伴隨疾病遺傳的，如血友病等，還有一些外表特徵如自然的紅發，也是先天遺傳的：這些都不是經驗學習所能塑造的。
  2. The principles of genetics with application to the study of biological function at the level of molecules, cells, and multicellular organisms, including humans

    教導遺傳學原理，並應用在學習分子、細胞、多細胞生物包括人類各層級生物上的功能。
  3. This article puts forward a solution named divide - assemble by deducing the size of bp neural network to overcome entering the local best point, the dividing process is that a big bp neural network is divided into several small bp neural networks, every small bp neural network can study alone, after all small bp neural networks finish their study, we can assemble all these small bp neural networks into the quondam big bp neural networks ; on the basis of divide - assemble solution, this article discusses the preprocessing of input species and how to deduce the size of bp neural network further to make it easy to overcome entering the local best point ; for the study of every small bp neural network, this article adopts a solution named gdr - ga algorithm, which includes two algorithms. gdr ? a algorithm makes the merits of the two algorithms makeup each other to increase searching speed. finally, this article discusses the processing of atm band - width distribution dynamically

    本文從bp網的結構出發，以減小bp神經網路的規模為手段來克服陷入局部極小點，提出了bp神經網路的拆分組裝方法，即將一個大的bp網有機地拆分為幾個小的子bp網，每個子網的權值單獨訓練，訓練好以後，再將每個子網的單元和權值有機地組裝成原先的bp網，從理論和實驗上證明了該方法在解決局部極小值這一問題時是有效的；在拆分組裝方法基礎上，本文詳細闡述了輸入樣本的預處理過程，更進一步地減小了bp網路的規模，使子網的學習更加容易了；對于子網的學習，本文採用了最速梯度? ?遺傳混合演算法（即gdr ? ? ga演算法） ，使gdr演算法和ga演算法的優點互為補充，提高了收斂速度；最後本文闡述了用以上方法進行atm帶寬動態分配的過程。
  4. It is very important to estimate the basic parameters in helicopter preliminary design. neural network ( nn ) has the advantages in estimating accuracy and generalization over traditional methods. however, there are some difficulties in using nn, e. g., how to select a proper network structure and the number of hidden layers. in this paper, structure and connection weight of a three - layer nn are optimized by genetic algorithm, and the optimized network is applied to helicopter sizing. the proposed method can not only give an optimal nn structure and connection weight, but also reduce the prediction error and has the capability of self - learning when the latest data are available. furthermore, this method can be easily applied to helicopter design systems

    在直升機初步設計階段估算其基本參數是很重要的.神經網路的通用性和精度比傳統的估算方法有更多的優勢，但是在應用神經網路時存在如何選擇合適的網路結構和隱層節點數目等一些困難.應用遺傳演算法優化三層神經網路結構和連接權重，並將優化得到的網路應用於直升機參數選擇中.該方法不但可以給出一個最優的神經網路結構和連接權重，而且降低了估算誤差，具有及時應用最新數據學習的能力.此外，該方法易於在直升機設計系統中得到應用
  5. " outside the wall blossomming inside the wall sweet ", this is a in our country wide popularity oral language, those who tell is some things, include the result that has obtained, also include to solve the new method of the problem, new technology, bequest of all sorts of admirable resource that still include interior, culture is waited a moment, oneself still do not know, outside however already from mouth to mouth, the target that already became everybody to go after or the model that make family study already

    「墻內開花墻外香」 ，這是在我國廣泛流行的一名口頭語，講的是有些東西，包括已經取得的成績，也包括解決問題的新方法、新技術，還包括內部的各種優良資源、文化遺產等等，自己還不知道，外面卻都已廣泛流傳，並已成為大家追求的目標或早已成為人家學習的榜樣。
  6. Compare to common model based on the flux analysis, this model describe the traffic network more reality. moreover, the efficiency and characteristic enable the result of optimization more precision and fleetly

    與基於流量分析的交通模型相比，該模型更加準確的描述了真實交通網路的運行情況，而遺傳演算法的自組織性和自學習性保證了優化過程的高效性。
  7. This class grounds students in some of the more classical techniques, as well as the latest techniques in genetic manipulation and its impact on neurobiology

    這個課程讓學生學習一些更經典的技術，以及在遺傳操縱方面的最新技術與遺傳操縱對神經生物學的影響。
  8. 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。
  9. In rsdm, binary patterns are replaced by real - valued patterns, accordingly avoiding the coding process ; the outer learning rule is replaced by regression rule, therefore the model has not only the ability of pattern recognition but the ability of function approximation. the prearrangement of the address array bases on the distribution of patterns. if the distribution of patterns is uniform. then the address array is prearranged randomly, otherwise predisposed with the theory of genetic algorithm and the pruneing measure so as to indicate the distribution of patterns and improve the network performance. non - linear function approximation, time - series prediction and handwritten numeral recognition show that the modified model is effective and feasible

    在rsdm中，以實值模式代替二值模式，避免了實值到二值的編碼過程：以回歸學習規則代替外積法，使該模型在具有識別能力的同時具有了對函數的逼近能力；地址矩陣的預置根據樣本的分佈採取不同方法，若樣本均勻分佈，則隨機預置，否則利用遺傳演算法的原理和消減措施來預置地址矩陣，使之反映樣本的分佈，改善網路的性能。
  10. Based on these graphic models an efficient routing algorithm, called ieacs ( intensified evolutionary ant colony system ) algorithm is suggested. ieacs merges the acs ( ant colony system ) and ga ( genetic algorithm ), thus features both the mechanism of cooperative learning of acs and the information - exchange capability of ga, is presented in this thesis. in case of asymmetric grid graph and grid - off graph

    然後，介紹了一種兼具生物仿生特性的蟻群和遺傳演算法特點的進化蟻群演算法，並對該演算法模仿蟻群的協同學習機制，以及遺傳演算法的優秀群體中的個體之間信息交換的策略進行了闡述，接著探討了該演算法在總體布線和斯坦納樹問題中的應用。
  11. In the algorithm level, currently various training algorithms of neural networks, including gradient algorithms, intelligent learning algorithms and hybrid algorithms, are comparatively studied ; the optimization principle of bp algorithm for neural networks training is analyzed in detail, and the reasons for serious disadvantages of bp algorithms are found out, moreover, the optimization principle of two kinds of improved bp algorithms is described in a uniform theoretic framework ; and the global optimization algorithms of neural networks, mainly genetic algorithm are expounded in detail, it follows that a improved genetic algorithm is proposed ; finally the training performances of various algorithms are compared based on a simulation experiment on a benchmark problem of neural network learning, furthermore, a viewpoint that genetic algorithm is subject to " curse of dimension " is proposed

    在演算法層，本文對目前用於神經網路訓練的各種演算法，包括梯度演算法、智能學習演算法和混合學習演算法進行了比較研究；對用於神經網路訓練的bp演算法的優化原理進行了詳細的理論分析，找到了bp演算法存在嚴重缺陷的原因，並對其兩類改進演算法-啟發式演算法和二次梯度演算法的優化原理，在統一的框架之下進行了詳盡的理論描述；對神經網路全局優化演算法主要是遺傳演算法進行了詳細的闡述，並在此基礎上，設計了一種性能改進的遺傳演算法；最後基於神經網路學習的benchmark問題對各種演算法在網路訓練中的應用性能進行了模擬研究，並提出了遺傳演算法受困於「維數災難」的觀點。
  12. Fourthly, leaning agent adjusts the domain model and user model adaptively by reinforcement learning and genetic algorithm

    第四，作者研究了學習agent使用強化學習、遺傳演算法自適應地調整領域模型和用戶模型。
  13. These will lay a foundation for students to learn plant taxonomy, plant physiology, plant ecology, plant resources, genetics, cytology, molecular biology, etc. in the future

    這將為學生以後學習植物分類學、植物生理學、植物生態學、植物資源學、遺傳學、細胞學、分子生物學等打下基礎。
  14. 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。
  15. Cytological genetic analysis revealed that the chromosome number of gynogenetic grass carp is 48 and the karyotype is 18m + 24sm + 6st, fundamental arm number ( nf ) is 90, just as those of the common grass carp

    此外，對雌核發育草魚細胞遺傳學分析證實所得雌核發育草魚的染色體數目為48條，其核型公式為18m + 24sln + 6st ;臂數為哪習o 。
  16. An improved genetic algorithm of wavelet neural network based on chaotic searching

    一種改進的基於混沌搜索策略的小波神經網路遺傳學習演算法
  17. ( 5 ) based on fuzzy control theory, fuzzy neural network ( fnn ) controller is introduced ; the changeable area and hierarchical genetic algorithm is used to training fuzzy neural network. simulation indicated that this designed fuzzy neural controller is effective

    （ 5 ）在模糊控制的基礎上，提出了模糊神經網路控制器，並用本文提出的變區域多層遺傳演算法與bp相結合進行網路學習，在理論上證明了遺傳學習演算法的收斂性。
  18. Another understanding about intrusion detection is viewing machine learning as a searching process, that is to say, intrusion detection is in essence the searching or approximation issue of intrusion rules in accordance to established searching strategy. after some concerned

    在基於遺傳學習的入侵檢測研究中，把機器學習看作一個搜索過程，即入侵檢測可視為基於訓練樣本集，按照既定的搜索策略對入侵規則的搜索或逼近問題。
  19. In designing a multi - structuring elements filter, combination rules and structuring elements of the morphological transform are determined automatically, and one kind of neural networks is taken for the filter, in optimzing structural parameters of the filter, three computation methods are designed respectively, by adopting some priori information in application fields to guide optimal structural parameter learning procedure, which are the bp adaptive learning algorithm, the heuristic genetic learning algorithm and the inductive simulated annealing learning algorithm

    在多結構基元濾波器設計中，通過學習人-機交互選定的目標樣本，自動確定形態變換的組合規則及其結構元素，最終以神經網路形式構成濾波器。在結構參數的優化學習中，利用應用領域的先驗知識，分別設計了自適應bp學習、啟發式遺傳學習和引導式模擬退火學習等三種最優化計算方法。
  20. The dissertation mainly aims at applying several active machine learning strategies to intrusion detection and systematically studies signal analysis techniques of intrusion detection based on statistical learning theory ( slt ), symbol inductive learning theory and genetic learning method. meanwhile, performance comparison and evaluation among intrusion detection techniques based on different machine learning strategies are presented according to computational learning theory and statistical hypothesis test methodology. intrusion detection is regarded as a pattern recognition problem in term of statistical learning theory ; i

    本文的主要工作是將目前幾種有生命力的機器學習策略應用於入侵檢測技術中，論文從入侵檢測的不同視角出發，系統深入地研究了統計學習理論、基於符號的歸納學習理論和遺傳學習方法在入侵檢測信號分析中的應用技術，並在可能近似正確（ pac ）學習框架下，利用計算學習理論和統計假設檢驗方法對基於不同機器學習策略的入侵檢測方法進行了性能比較和評估。
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