絕對收斂性 的英文怎麼說

中文拼音 [juéduìshōuliǎnxìng]
絕對收斂性 英文
absolute convergence
  • 絕 : Ⅰ動詞（斷絕） cut off; sever Ⅱ形容詞1 （完全沒有了; 窮盡; 凈盡） exhausted; used up; finished 2 ...
  • 對 : Ⅰ動詞1 （回答） answer; reply 2 （對待; 對付） treat; cope with; counter 3 （朝; 向; 面對） be tr...
  • 收 : Ⅰ動詞1 （把攤開的或分散的事物聚集、合攏） put away; take in 2 （收取） collect 3 （收割） harvest...
  • 斂 : Ⅰ動詞1 （收起; 收住） hold back; keep back 2 （約束） restrain 3 （收集; 徵收） gather; collect; ...
  • 性 : Ⅰ名詞1 （性格） nature; character; disposition 2 （性能; 性質） property; quality 3 （性別） sex ...
  • 絕對 : absolute
  • 收斂 : 1 （減弱或消失） weaken or disappear 2 （約束言行） restrain oneself 3 [數學] convergence; constr...
  1. Theorem 9 ( rearrangement theorem ) the terms of an absolutely convergent series can be rearranged without affecting either the convergence or the sum of the series

    定理9 （可交換性定理）絕對收斂級數經改變響的位置后構成的級數也收斂，且與原級數有相同的和（即絕對收斂級數具有可交換性） 。
  2. 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。
  3. A kind of complete convergence of sums for negatively associated sequences of non - identically distributed random variables, in the second chapter, is obtained and the requirement of known results are weakened to the condition that absoluted moment - larger than zero - is finite. the strong convergence of negatively associated sequences of non - identically distributed random variables is discussed in the third chapter. in the fourth chapter, after extend the laws of the iterated logarithm of strong stationary case to weak stationary case, we obtain the strong convergence rate for negatively associated sequences of non - identically distributed random variables in linear models

    其中第二章討論了一類不同分佈的na列的加權和的完全收斂性，我們把已有的結果對矩的要求放寬到了只要求大於0的絕對矩有限的情形；第三章討論了不同分佈的na列的加權和的強收斂性；第四章首先把文[ 10 ]的關于na的重對數律由強平穩的情形推廣到了弱平穩不同分佈的情形，然後得到了線性模型中不同分佈的na誤差列的收斂速度。
  4. Image and intuition have been playing a impor tant role in the creation of science, being a irrational element, it can direct promote the creation of between the convergent thinking and divergent thinking is necessary

    同樣，教學作為一種理性活動，並不意味著它就絕對排斥非理性因素。關於思維，則是要求在發散性思維與收斂性思維之間保持一種必要張力。
  5. This algorithm improves confidence in se by estimating parameters and states at the same time. simulation results on test power systems which range in size from 4 to 118 buses, have shown the virtues as follows : getting unbiased estimation without detecting and identifying bad data in measurements ; solving state and parameter estimation for power system with good convergence and excellent robust property ; increasing the numbers of iterations a little bit with the test systems expanded ; estimating many transformer taps simultaneously and remaining the main state estimation ; keeping the estimated relative error within + 0. 1 % and processing efficiently equality constraints and ill condition with polynomial complexity

    對ieee ? 4 118節點系統和廣西主網進行的模擬結果表明： l1范數估計具有不良數據拒絕特性，當量測量中存在不良數據時，該演算法在不經檢測和辨識不良數據情況下仍是無偏估計，具有良好收斂性，所需迭代次數隨著問題規模擴大而增長極小；能夠同時估計多個變壓器抽頭，並保持狀態估計主體；在滿足可觀測性條件下，估計的相對誤差保證在0 . 1以內；能夠有效處理等式約束和病態條件，並具有多項式時間性。
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