訓練樣本 的英文怎麼說

中文拼音 [xùnliànyàngběn]
訓練樣本 英文
training sample
  • 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...
  • 樣 : Ⅰ名詞1. （形狀） appearance; shape 2. （樣品） sample; model; pattern Ⅱ量詞（表示事物的種類） kind; type
  • 本 : i 名詞1 （草木的莖或根）stem or root of plants 2 （事物的根源）foundation; origin; basis 3 （本錢...
  • 訓練 : train; drill; manage; practice; breeding
  • 樣本 : sample book; specimen; advanced copy; sample; muster; scantling; instance; statistics
  1. The experimental results indicate that it is easy to be realized, can save the calculating cost and improve the constringency speed

    試驗結果表明，用粒子群演算法來訓練樣本集具有容易實現、節省計算成本和提高收斂速度等優點。
  2. Mining classification rules is a procedure to construct a classifier through studying the training dataset. it is a very important part of data mining and knowledge discovery

    分類規則挖掘則是通過對訓練樣本數據集的學習構造分類規則的過程，是數據挖掘、知識發現的一個重要方面。
  3. Useful training samples result fro m the experiment and the calculation for the emulational faults via supersap software

    對懸臂梁的故障用supersap軟體進行了模擬數值計算，獲得訓練樣本。
  4. 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。
  5. First, realized a wegener - willie distribute based network traffic anomaly detection algorithm. we make use of wegener - willie distribute to analyze the inherent time - frequency distribution characteristics of the traffic flow signal. then according to the experience of analysis on historical flow, we construct a normal flow training sample aggregation and a abnormal flow training sample aggregation

    通過魏格納-威利分佈分析網路流量信號在時頻分佈上所反映出的內在特點，根據歷史流量的經驗構造正常流量和異常流量兩個訓練樣本空間，通過k最近鄰分類演算法將帶檢測流量信號的時頻分佈與訓練樣本進行比較，完成對檢測樣本的自動分類識別。
  6. By using the air temperature measured by thermocouples and the glass temperature measured by infrared thermometer as the training sample, a model for prediction of the temperature parameters for the float glass in lehr was created based on the ameliorated bp neural network

    摘要利用熱電偶測得的退火窯中空氣溫度和紅外測溫儀測得的玻璃表面溫度作為訓練樣本，建立了基於bp神經網路的玻璃溫度預測模型。
  7. As to the selection of neural network input node, not only is related historical load was introduced as ? the drilling sample, but also influence of temperature and weather sensitive factors to the load variance is considered. 4

    在神經網路輸入節摘要點的選擇方面，除了引入相關歷史負荷作訓練樣本外，還考慮了溫度、氣候敏感因素和特徵日對負荷變化的影響，提高了負荷預測的精度。
  8. In order to verify the feasibility of ann, adopting same training sample the author establishes quadratic curve model and index model of tourism foreign exchange income and cubic curve model and index model of total inbound tourist quantity

    為了驗證人工神經網路模型的可行性，筆者用同樣的訓練樣本分別建立了旅遊外匯收入二次曲線模型、指數曲線模型和入境遊客三次曲線模型、指數曲線模型。
  9. Neural network method has generalization capability, and is widely used in pattern recognition. generalization means : trained with a training examples set, the network can also recognize examples never met before

    神經網路的泛化能力是指:用一組訓練樣本對神經網路進行訓練后，網路對訓練階段未曾見過的樣本也能正確分類。
  10. Firstly, the paper, combining the characteristic of synchronous pulse bursts and inhibition with the modified pcnn model, presents a way of finding the foveation points in the images adaptively and effectively, and simulates the human vision system. secondly, pcnn is extended to pcnns, based on the properties of information couple and transmission, an algorithm that is used to fuse images of the same target got by several sensors to an image is presented to simulate the human vision system. thirdly, combining the properties of synchronous pulse bursts, capture, and transmission and competition of waves, the paper presents two ways of classification, one is an algorithm based on the properties of neuron to capture and inhibit to classify the data taking on any complex unlinear distribution robustly, the other is based on the restricted distance and modified of the former to remove the influence of inferior samples in classification ; fin ally, based on the accumulative difference pictures, and the forming and transmission of pcnn wave, selecting and controlling the direction of autowave by connecting the neighbouring neurons selectively, the paper presents a way to simulate the tracks of moving object and detect the moving direction

    首先結合pcnn的同步脈沖發放和側抑制特性，提出了基於改進型pcnn的圖像凹點檢測演算法，該演算法是一種自適應而有效的圖像凹點檢測方法，並且較好地模擬了人類視覺系統；然後，結合信息傳遞和信息耦合特性，將pcnn擴展成pcnns （ pcnn網路群） ，提出了一種基於pcnns的圖像融合演算法，能夠將多個傳感器獲取的同一目標的圖像信息融合到一幅圖像中，有效模擬了人類視覺系統；另外，結合pcnn的同步脈沖發放特性、捕獲特性和波的傳播競爭特性，開拓地將pcnn用於模式分類中，提出了基於耦合神經元點火捕獲抑制特性的分類方法和改進的約束距離下的pcnn分類方法，前者可實現對樣本空間中任意復雜分佈訓練樣本的穩健非線性分類，而後者能夠消除訓練樣本中刺點對分類的影響；最後，結合累積差分圖像思想、 pcnn波的形成與傳播特性，通過各神經元之間連接取向來選擇與控制自動波的流向，將pcnn用於運動視覺分析中的運動軌跡模擬及運動方向檢測。
  11. Additionally, the optimum structure and parameters of the support vector machine can easily be determined by the learning process, however the neural networks can not. an information gain of signature signals is introduced to assess the contribution of the signature signals to diagnosing faults in rotating machines

    同時發現，存在一個最佳訓練樣本比例值，在該比例值上，不同核函數支持向量機的故障診斷錯誤率均趨于穩定，也就是說這個比例值確定了在保證故障診斷準確率的條件下，所需要的最少訓練樣本數。
  12. The system firstly learns the domain training samples by using thesaurus to process word - separation and word - frequency statistics. according to word - frequency distribution, it chooses the feature collection and their weights to formulate feature vector and generate domain model and user model

    系統首先對領域訓練樣本進行學習，利用領域詞典對訓練文本進行詞條切分和詞頻統計，並根據詞頻分佈，提取代表採集目標的特徵項集和相應的權重，生成特徵矢量，形成初始領域模型和用戶模型。
  13. As the ratio of peak samples to total training samples is low, the prediction accuracy of peak load is poor when applying support vector machine ( svm ) model to predict a periodical load

    在應用svm模型于具有周期性負荷的預測時，由於在訓練樣本中峰值或谷值樣本所佔比例很少，從而導致峰值荷載預測精度不高。
  14. In the paper, based on the existing literature research foundation an analog circuit catastrophic fault location approach by using feedforward networks with back ? propagation learning is realized. by this approach, the simulation require ments before test are reduced because fewer training samples are needed, and the fault location process is fast. this method is very efficient in location of single hard fault wit component tolerances. the measureme nt space feature and the general characterization concept of single and double soft fault in linear circuits are presented. according to this concept, a linear circuits soft fault location approach using subhidden layer bpnn is established with element tolerance, and it is shown that this approach is successful in fault location. a double fault feature extraction.,

    本文在現有文獻理論研究的基礎上實現了採用bp演算法前向多層神經網路對直流測試下模擬電路硬故障的診斷方法。其特點是採用少量典型特徵樣本作為bp網路的訓練樣本，獲得訓練樣本的代價小，減少了測前工作量，同時診斷速度快，在考慮元件容差時仍有好的診斷效果。文中介紹了線性電路單一軟故障和雙軟故障所具有的電壓增量空間特性和統一特徵概念。
  15. According to the utilized face database, three facial expression categories are defined : neutral, happiness and anger. the categorization architecture is based on a som. in order to eliminate influence of initial values and sequence of input examples in som, supervised learning is introduced into the training stage

    分類器的設計採用的是基於自組織神經網路的方法，為了克服傳統的自組織映射神經網路的訓練結果容易受訓練樣本的輸入順序和權值初值影響，而導致訓練結果不符合期望的問題，因此，在訓練過程中引入了監督機制，以使訓練結果與期望相符。
  16. The dada acquisition and pressure survey methods during under - balanced drilling are researched, which provide the foundation for obtaining training samples for neural network

    研究了欠平衡鉆井數據採集和壓力測量方法，為神經網路獲取訓練樣本奠定了基礎。
  17. The output information of single classifier has three forms of abstract, rank and measurement single classifier supplies both the unknown pattern classifying information on the measurement level and the wrong classifying distribution information of the training samples on the abstract level, which are used to design the fuzzy multiple classifiers combination method

    單個分類器的輸出信息有三種表現形式：符號層、排序層、度量層。應用單個分類器在度量層次上，對未知模式的分類信息；在符號層次上，訓練樣本的錯分類分佈狀況，設計了模糊多分類器組合方法。
  18. Under ideal conditions, adaptive array signal processing methods can get excellent performance and adaptive beamformers provide an improvement in array output signal - to - interference - plus - noise - ratio ( sinr ) in comparison with conventional beamforming. in practical operating circumstances, the performance of adaptive array signal processing methods degrade extremely due to existing errors

    但是，在實際系統中總存在有誤差，包括自適應訓練樣本有限次快拍引起的協方差矩陣的估計誤差和各種系統誤差，誤差使得實際陣列流形與理想陣列流形存在差異，這時自適應陣列信號處理的性能會急劇下降。
  19. And that, while the training samples is few and there is random error, ann is much better than ordinary statistical models. generally speaking, while the tourism demand statistical data is for a short period time, and tourism demand is disturbed by many unpredictable factors, ann is a more superior model

    一般而言，旅遊需求統計數據時間較短（也就是說可供「學習」的訓練樣本小） ，而且旅遊需求還受到眾多不可預知因素的干擾，所以在進行旅遊需求預測時用神經網路是一個比較優越的模型分析方法。
  20. ( 5 ) a series of design methods of classifiers are proposed, including the classifier based on the generalized inverse and the probabilistic reasoning method ( prm ), a new self - adaptive kohonen clustering network which overcomes the shortcomings of the conventional clustering algorithms, and the fuzzy neural classifier. the experimental study efface recognition is presented based on the combination of multi - feature multi - classifier. ( 6 ) this paper proposes a hybrid feature extraction method for face recognition, which is a combination of the eigen matrix, fisher discriminant analysis, and the generalized optimal set of discriminant vectors

    （ 5 ）對圖象分類器設計方法進行研究，主要包括：提出了一種基於廣義逆和概率推理的分類器設計方法；提出了一種新的自適應模糊聚類演算法；提出了基於模糊神經網路的分類器設計方法；並對多特徵多分類器組合方法在人臉識別中進行實驗研究； （ 6 ）提出了一種只要一個訓練樣本就能解決人臉識別問題的新方法，該方法結合了特徵矩陣、 fisher最優鑒別分析和廣義最優鑒別分析方法的優點。
分享友人
分享到 Line

分享到臉書