訓練過度 的英文怎麼說

中文拼音 [xùnliànguòdù]
訓練過度 英文
overtrain
  • 訓 : Ⅰ動詞1 （教導; 訓誡） lecture; teach; train 2 （解釋） explainⅡ名詞1 （準則） standard; model; ex...
  • 練 : Ⅰ名詞1 （白絹） white silk 2 （姓氏） a surname Ⅱ動詞1 （加工處理生絲） treat soften and whiten s...
  • 過 : 過Ⅰ動詞[口語] （超越） go beyond the limit; undue; excessiveⅡ名詞（姓氏） a surname
  • 度 : 度動詞[書面語] （推測; 估計） surmise; estimate
  • 訓練 : train; drill; manage; practice; breeding
  • 過度 : excessive; over; undue; ana-; hyper-
  1. Advices : during the training time you should use the rneasurment of heartrate very efficenly to control the arrangement of burthen intensity

    建議：在運動訓練過程中應該運用心率的測量有效的控制負荷強度的安排。
  2. Firstly the patterns of the multifingered hands are detailed, eight patterns are defined. the classical bayes method is used in the classification of pre - grasp of multiple fingers based on three patterns which are grasping, holding and pinching. based on the eight pre - grasp patterns, bp neural network is applied in the classification of the pre - grasp of multifingered hands and gets a good effect. the method solves the shortcoming input sample relying on the propobility density and simplified the un - insititution characters extraction. in this paper, support vector machine ( svm ) and binary - tree with clustering is applied in the classification. this method can solve the slow speed and effect with fewness sample in the classification, achieving a good effect. in this papper, we extract the characters of the regulation object with geometry characters and extact the unregulation object with the image analysis

    此法解決了輸入樣本依賴物體的概率密度的特點，簡化了分類特徵提取的不直觀性。本文還採用了支持向量機（ svm ）和聚類二叉樹相結合的方法對機器人手預抓取八類模式進行分類，解決了預抓取模式分類訓練速度過慢以及在分類中樣本數量偏少而影響分類效果的問題，得到了較高的正確率。本文對預抓取幾何形狀規則的物體採用直接提取其幾何特徵，對于預抓取幾何形狀不規則的物體採用圖像分析的方法進行特徵提取。
  3. For this reason, during the delivery and the exercise period, the training and practise on the illusive action should need more concern by both the trainer and the trainee

    為此，教練員和運動員在教學和訓練過程中，應當對假動作的培養和訓練給予高度重視。
  4. As a response to some swordsmanship practitioners who concentrate on delivering hard blows in sparring, here we made a video to investigate how hard one has to hit in order to inflict effective damage in unarmored combat and the difficulty in crashing through opponent s defense with hard blows

    為了回應一些在對打訓練過程中，著重向對方施予重擊的練習者的疑問，我們製作了一段影片，探討在史實無裝甲刀劍格鬥當中，造成有效傷害的所需力度，以及重擊對于打破對方防線的有效性。
  5. Its aim is to train students to grasp activities of knowledge, which can become active experiences of obtaining scientific knowledge, practical training course of scientific methods and skills, and while students being promoted in perceiving, make them learn how to share and cooperate with others

    旨在使學生掌握科學知識的活動，成為獲得科學過程的積極體驗、科學方法與技能的實踐訓練過程。在促進學生認知發展的同時，學會與人分享及密切合作、培養正確的科學態度和科學精神。
  6. Compared with the classical bp algorithm, robust adaptive bp algorithm possesses some advantages as following : ( 1 ) increasing the accuracy of the network training by means of using both the relative and absolute residual to adjust the weight values ; ( 2 ) improve the robustness and the network convergence rate through combining with the robust statistic technique by way of judging the values of the samples " relative residual to establish the energy function so that can suppress the effect on network training because of the samples with high noise disturbances ; ( 3 ) prevent entrapping into the local minima area and obtain the global optimal result owing to setting the learning rate to be the function of the errors and the error gradients when network is trained. the learning rate of the weights update change with the error values of the network adaptively so that can easily get rid of the disadvantage of the classical bp algorithm that is liable to entrap into the local minima areas

    與基本bp演算法相比，本文提出的魯棒自適應bp演算法具有以下優點： （ 1 ）與魯棒統計技術相結合，通過訓練樣本相對偏差的大小，確定不同訓練樣本對能量函數的貢獻，來抑制含高噪聲干擾樣本對網路訓練的不良影響，從而增強訓練的魯棒性，提高網路訓練的收斂速度； （ 2 ）採用相對偏差和絕對偏差兩種偏差形式對權值進行調整，提高了網路的訓練精度； （ 3 ）在採用梯度下降演算法對權值進行調整的基礎上，通過將學習速率設為訓練誤差及誤差梯度的特殊函數，使學習速率依賴于網路訓練時誤差瞬時的變化而自適應的改變，從而可以克服基本bp演算法容易陷入局部極小區域的弊端，使訓練過程能夠很快的「跳出」局部極小區域而達到全局最優。
  7. For this, trainer and athlete should give high value to development and training of false action in the teaching and the training process

    為此，教練員和運動員在教學和訓練過程中，應當對假動作的培養和訓練給予高度重視。
  8. In this dissertation, two kinds of optimization, methods are proposed. firstly, only these linking weights corresponding to the control rules that affect the control performance significantly are updated in order to reduce the compute works and speed up the training progress. secondly, the updating step is adjusted adaptively in accordance with the error and the change of error of the system based on the t - s model to get better performance

    針對模糊神經網路控制器一般存在著在線權值調整計算量大、訓練時間長、過度修正權值可能導致系統劇烈振蕩等缺點，提出了兩種模糊神經網路控制器的優化方法：在線自學習過程中僅對控制性能影響大的控制規則相關的權值進行修正，以減小計算量，加快訓練速度；基於t - s模糊模型，根據偏差及偏差變化率大小動態自適應調節權值修正步長，抑制控制器輸出的劇烈變化，避免系統發生劇烈振蕩。
  9. Thomas, except for the ferocious, muscular, overtrained body, looked surprisingly the same.

    托馬斯，除了那個雄赳赳的，肌肉發達的，訓練過度的身軀看來還是老樣子，簡直出人意外。
  10. The inevitable consequences of “ more is better ” are burnout, overtraining, illness, and injury

    「多就是好」將導致不可避免的結果包掛力竭、訓練過度、生病和運動傷害。
  11. 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。
  12. The fault diagnosis example shows that the difficulty of neural network training is diminished and the fault diagnosis accuracy can reach more then 99 % when faults overlaps exist

    通過診斷示例表明，該方法在故障類存在重疊時，降低了神經網路的訓練難度，故障診斷的正確率達到99 %以上。
  13. In order to meet the needs of changing situation in modern education, this essay tries to adopt some methods, such as summary comparison, and investigation, under the direction of marxist methodology. it regards the tactics of chinese spoken communication teaching at the stage of compulsory education of our country as the research object. under the prerequisite of analyzing the current situation of compulsory education of our country and opening out the realistic meaning of strengthening spoken communication, this text describes the intension and the goal of training of spoken communication to the stage of compulsory education

    為了適應現代教育形勢發展的需要，本論文寫作將在馬克思主義方法論的指導下，擬用經驗總結、比較借鑒及調查研究等方法，以我國義務教育階段的語文口語交際教學策略作為研究對象，在分析我國義務教育階段口語交際教學現狀及原因、揭示加強口語交際教學與訓練的現實意義的前提下，描述義務教育階段口語交際訓練的目標規定；根據口語交際訓練策略的內涵及其特徵，確立口語交際訓練過程中應持的策略，包括原則策略、梯度策略、人才策略、內容策略、方法策略、評價策略等。
  14. Working like mad to reach your peak but getting nowhere ? you might be overtraining

    工作相似的瘋狂到達你的山頂但是無處得到？你可能訓練過度。
  15. An athlete who enjoys training will get far more benefits from it than one who is always on the edge of overtraining

    與總是在訓練過度的邊緣上的人比較，享受訓練的運動員將獲得更多的好處。
  16. Using the first order and the second order derivative information of the network error function to the learning rate factor / / and the momentum factor ?, dynamic optimization of the learning rate factor / / and the momentum factor ? is obtained during the network training process, which efficiently speeds up the network learning rate

    並利用網路誤差函數對訓練速率系數和沖量系數的一階和二階導數信息，在網路訓練過程中動態優化調整訓練速率系數和沖量系數，有效地加快了網路的訓練速度。
  17. When gun - powder category is different, the sample of training neural network needs change and network parameters 、 need adjustment in the training process according to the requirement of velocity. but the genetic algorithms parameter and training algorithm of neural network will keep the same

    當藥劑品種不同時，需要改變網路的訓練樣本庫對網路進行重新訓練，同時網路參數、應根據對網路收斂的速度要求在訓練過程中進行調整，而遺傳演算法的相關參數和網路的訓練演算法不變。
  18. This article researched about the method of heartrate measuring and the relationship between heartrate and intensity by using. references logic analysis and interview with experts

    摘要本文通過文獻資料查閱、邏輯分析、專家訪談等方法，對心率測量方法、心率與訓練強度的關系等問題進行了研究。
  19. To overcome the difficulty in determining the rbf center numbers and spread, a kind of generalized genetic algorithm is introduced, which follows the analysis of the basic rules of genetic algorithm. the new hybrid algorithm determines the center numbers and spread adaptively to reach the optimal tradeoff between the training accuracy and the generalization, so it increases the prediction accuracy of the model

    針對建模過程中出現的rbf中心和寬度難以確定的難點，在分析遺傳演算法機理和基本演算法的基礎上，提出了使用廣義遺傳演算法對rbf神經網路模型的中心和寬度進行自適應選擇，從而達到模型訓練精度和范化能力的一個最優的平衡，從而提高模型的預報精度。
  20. In the training this network ' s topology structure and mapping function are the most optimal all along. and the learning velocity and precision are improved. with this recognition method shape pattern information and magnitude can be received rapidly and exactly, which can provide reliable data for later shape controlling

    本文將優化網路用於板形信號的模式識別，建立了6輸入、 3輸出的識別網路模型，該網路性能在訓練過程中始終保持最優，能夠達到最佳結構，加快了學習速度和訓練精度，可以快速、準確求出板形缺陷的模式信息及數值大小，為后續板形控制調節量的設定提供了可靠依據。
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