learning coefficient 中文意思是什麼

learning coefficient 解釋
學習系數
  • learning : n 學，學習；學問，學識；專門知識。 good at learning 善於學習。 a man of learning 學者。 New learn...
  • coefficient : adj. 共同作用的。n. 1. 共同作用；協同因素。2. 【數，物】系數，率；程度。
  1. Based on this kind of relations between the topological structures and the content distributions we study the web modelling, community identification and some related application problems in detail : first, after some existed characteristics of the web topology are verified, some new characteristics are discovered : the high clustering property in micro - topology ( high average gathering coefficient ), the obvious mapping relation between the topological struture and the content in micro - level 、 linear irrelevant between the degree distribution of network nodes and the relative degree distribution of contents etc. then after analysis the topology of the complex network and the network modeling, the muti - scale determinism is proposed, especially for the information network a web evolvement model ( prcp model ) that fused the node authority and the node correlation is proposed. the model deduction, evolving learning verification and large scale experiment proof indicate that the model can explain the micro - topology centralizing phenomena, can imitate the mapping relation between the network connecting distribution and network content relative distribution and also can predict the mapping relation between the topology clustering and content clustering

    本文在詳細觀察了web網路的拓撲結構特徵以及拓撲結構與內容分佈相互關系的基礎上，以信息網路的物理連接拓撲結構與節點內容相關度分佈之間的相互關系為主線，從網路特徵、網路建模、社區分析及相關應用方面問題進行了深入細致地探討:首先在驗證了前人提出的web網路拓撲結構特徵基礎上，進一步發現了信息網路所具有的一些新特徵: 1 ）網路微觀顆粒度的拓撲結構聚團與內容聚團存在明顯的映射關系，具體包括節點之間的物理連邊概率與節點之間的內容相關度成指數比例關系、節點形成三角形拓撲結構的概率與節點內容相關緊密程度之間同樣具有一種指數比例關系; 2 ）網路節點連接度整體分佈與節點內容相關度整體分佈是線性無關的; 3 ）網路微觀拓撲結構中的存在很強的集聚性（平均聚團系數很高） 。
  2. Furthermore, utilizing the characteristic that filtering error covariance expresses filtering precision and the principle of information conservation, the dynamic and reasonable distribution of distributed tracks weight coefficient is accomplished. jerk model and strong tracking filter is organically assembled, and based on spatio - temporal synthetically analysis and lme, a self - learning estimation method of the system measurement variance is given. the method improves obviously the

    3 、將jerk模型與強跟蹤濾波演算法有機地結合，並利用時空綜合分析和極大似然估計的思想推導出了一種系統量測方差自學習修正方法，以優化強跟蹤濾波演算法中次優漸消因子和濾波增益的在線選擇，同時根據多傳感器數據融合具有改善濾波精度的性質，進而給出一種基於jerk模型的多傳感器數據融合演算法。
  3. This paper presents the concept of knowledge transformation coefficient, learning ability coefficient and knowledge rigidity etc. and the appraisal model of the competitive power correspondingly are developed

    本文提出了知識轉化系數、學習能力系數和知識剛度等概念，並相應建立了競爭能力的評價模型。
  4. Based on the formal measurement table applying the 2 ( superscript nd ) pre - test on 351 students and conducting the examination of the credibility and validity to construct the credibility and validity of the measurement table, the coefficient of inner consistency of individual element in this table was. 673 ~. 892, and the coefficient of re - test after the interval of two weeks was. 697 ~. 930, with the performance of physical education learning to prove the discriminate validity and criterion - related validity, and with the element analysis to examine the construction validity, the sum of variable amount which could be interpreted was 59. 06 %, the outcome showed this table was having good credibility and validity that could provide physical education teachers to realize the learning strategies of university students

    以正式量表對351位學生進行第二次預試，再進行信度與效度的考驗，以建構量表的信效度，本量表各因素的內部一致性系數為. 673 ~ . 892與隔二周的重測信度為. 697 . 930 ，以體育學習表現來驗證辨別效度及效標關聯效度，以因素分析考驗建構效度，所能解釋總變異量為59 . 06 % ，結果顯示本量表具有良好的信效度，確實可以提供國內體育教師了解大學生的體育學習策略。
  5. The lyapunov function is used to analyze the convergence of the general learning rule, and it is proved in theory that the general learning rule has the inherent factor which adjusts the coefficient values to gain the minimum error

    通過理論推導，用李雅普諾夫函數分析和驗證通用參數學習規則的學習收斂性，揭示參數學習演算法朝最小誤差方向調整參數的內在因素。
  6. In this paper, the classical ann approach is improved because of the introduction of inertial coefficient. now the ann can use a bigger rate of learning. with the introduction of former k times sample values, the improved ann method can only used to detect harmonic currents

    本文對傳統的ann法進行了改進，通過引入慣性系數的方法，提高了人工神經元自適應的學習率，並採用前k次采樣值，使得ann法用於檢測畸變電流中諧波電流。
  7. In order to improve the learning speed of ann, a revised bp algorithm is adopted by using smooth coefficient and forgetting coefficient

    為加快神經網路的學習速度，本文採用改進的bp演算法，引入平滑系數和遺忘系數。
  8. The proposed scheme uses a fuzzy inference system to verify the learning coefficient and the momentum term in neural network. to test this approach, simulation results are shown in the end of the paper

    本文採用了基於模糊推理的bp學習演算法，利用模糊推理系統動態地調整bp演算法的學習率和動量因子，彌補了神經網路的不足。
  9. In view of insufficient number of fault samples in process industry, support vector machines ( svm ) with the capacity of sufficient learning from a limited training set are used to identify the projection coefficient matrix of te process, and satisfying results are also obtained

    而考慮到實際工業過程故障數據都是少量的，支撐向量機在小樣本學習方面具有良好的泛化能力。利用支撐向量機對te過程的投影系數矩陣進行識別，也獲得較滿意的結果。
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