自組織映射 的英文怎麼說

中文拼音 [zìzǔzhīyìngshè]
自組織映射 英文
self-organization mapping
  • 自 : Ⅰ代詞（自己） self; oneself; one s own Ⅱ副詞（自然;當然） certainly; of course; naturally; willin...
  • 組 : Ⅰ名詞1 （由不多的人員組成的單位） group 2 （姓氏） a surname Ⅱ動詞（組織） organize; form Ⅲ量詞（...
  • 織 : 動詞（編織） knit; weave
  • 映 : 動詞1. （因光線照射而顯出物體的形象） reflect; mirror; shine 2. （放映） project a movie
  • 射 : Ⅰ動詞1 （用推力或彈力送出） shoot; fire 2 （液體受到壓力迅速擠出） discharge in a jet 3 （放出） ...
  • 組織 : 1 （組織系統） organization; organized system 2 （組成） organize; form 3 [紡織] weave 4 [醫學] [...
  1. A sofm algorithm for vector quantizing

    基於自組織特徵映射的矢量量化方法
  2. A real rough set space and the concepts of real lower and upper approximation corresponding to real - valued attributes is studied. a rhombus neighborhood for som is proposed, and the combination of som and rough sets theory is explored in the dissertation. according to the distance between the weight of winner node and the input vector in the real rough sets space, some new weights learning rules are defined

    本文提出採用菱形鄰域代替一般的方形鄰域，可以減少待修正權重的數目;並利用實數粗糙空間的下、上近似集的精確概念劃分自組織映射的輸出結果，使得改進后的映射結果中各類樣本點之間有明顯的間隔，易於進行分類識別。
  3. Finally, a typical two classes example of two classes natural spearmint essence was employed to verify the effectiveness of the proposed approach cca - svm. the classification accuracy is much better than that obtained by svm alone or correlative component analysis - self - organizing map ( cca - som ) networks

    然後將其成功地應用在建立留蘭香的分類器模型上，它的訓練與預浙江大學博士學位論文測分類精度比svm方法、分類相關成分分析一自組織映射網（ cca一som ）方法都有明顯提高。
  4. Applying the classical pattern recogtiition theory anci ftiflcial neural networks method, this paper proposes the analog fault diahoes priricip1s with backward - propagation neural network ( i3pnn ) arid self - or ~ anizing feature map ( sofm ) neural network algorithm implementation

    本文提出了模擬電路故障診斷前向多層誤差反傳（ bp ）網路和自組織特徵映射（ sofm ）網路的演算法實現方法。
  5. The innovations of this thesis can be summarized into three points. firstly, the average relative velocity is introducd into a novel adptive weighted clustering algorithm as one important parameter of weight, then it increases the stability and self - adaptability of cluster head. secondly, a new approach to calculating weight is suggested by integrating subjective and objective factors. it is verified by comparison with other approaches to selecting weight. thus the velocity of weight responding to the changes of network topology is increased. finally, using a som neural network to create a classifying model enables every node to learn to identify by itself the role in manet

    本文的創新點有三個:首先本文在wca和aow分簇演算法的基礎上，引入了平均相對移動速度作為權值重要的參數，提出了一種新的基於權值的自適應分簇演算法，提高了簇頭在移動中的穩定性和自適應性;其次，提出了利用主客觀綜合賦權法確定權重的權值計算方法，通過與其他權重選擇方法比較，網路結構變化的權值響應速度得到了改進;最後，論文利用自組織特徵映射神經網路建立分類模型，使得網路中的節點可以自學習地確定簇中角色。
  6. Then analyze typical noises of the network based on bispectrum, get the wave characteristics according to bi - coherence function, and carry on the clustering analysis based on self - organizing feature mapping, so it realizes the clustering identification of the typical noises of medium - voltage power line

    而後對電網的典型噪聲進行雙譜分析，並通過雙相干函數提取其波形特徵，利用自組織特徵映射神經網路進行類聚分析，從而對特定中壓配電網的各種電網噪聲干擾進行分類識別。
  7. Since the images of a human face lie in a complex subset of the image space that is unlikely to be modeled by a single linear subspace, we use a mixture of linear subspaces to model the distribution efface and non - face patterns. in the other words, we used fisher linear discriminator to project samples from a height dimensional image space to a lower dimensional feature space

    對于背景復雜的人臉圖象的檢測，使用單個線性線性子空間很難準確地區分出人臉和非人臉模式，因此，本文使用混合線性子空間對人臉和非人臉樣本的分佈進行建模，在利用自組織映射神經網路標識人臉和非人臉樣本的基礎上構建一個fisher人臉檢測器。
  8. We discussed some statistical feature extraction methods, and the application of assom neural network in feature extraction

    我們研究了幾種統計特徵提取方法，和基於自適應子空間自組織映射（ assom ）神經網路提取字元的特徵提取方法。
  9. Examples of clustering iris data and alerts in intrusion detection also proved the good performance of the ksom method

    對iris數據集和入侵檢測報警數據的聚類也證明了核自組織映射聚類方法的良好性能。
  10. The experimental result shows that the ksom method can cluster the data with non - spherical shapes such as annular shape, and the cluster precision can reach 99. 8864

    實驗結果表明，核自組織映射聚類對于非橢圓型的類分佈數據，如環形數據，聚類正確率也能夠達到99 . 8864 % 。
  11. 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。
  12. To quicken convergence and improve model precision, a new algorithm is presented in this paper, which utilize construct orderliness property of self - organization feature maps ( sofm ), divide system input space and adopt 1 order or 2 order local model in each subspace individually instead of a global model

    為了提高收斂速度和模型精度，本文利用自組織映射網路拓撲有序特性，對系統輸入空間進行分割，在子空間中採用多個局部一階線性模型或二階模型代替全局模型的局部化方法。
  13. In order to research the spatial distribution law of the regional industrial structure, the self organization maps method is used to cluster and analyze the regional data in china 2003

    摘要為了研究地區產業結構的空間分佈規律，採用自組織映射的聚類方法，對2003年中國省級地區的產業結構進行了聚類分析。
  14. Combining self - organizing feature map with support vector regression based on expert system

    自組織映射演算法與基於專家系統的支持向量回歸的結合
  15. Improvement to color quantization based on self - organizing maps for computer graphics partition

    一種用於計算機圖形分離的自組織映射彩色量化方法
  16. The clustering result shows that the som method ran cluster the regional industrial structure into five categories in the output layer

    結果表明，自組織映射的方法能夠將地區產業結構在輸出層聚成5個區域。
  17. The idea of kernel - based learning method is applied to self organizing map ( som ) clustering, and an algorithm of kernel self - organizing map ( ksom ) clustering is proposed

    摘要將核學習的方法應用於自組織映射聚類中，提出了一種核自組織映射聚類演算法。
  18. This paper proposes a novel clustering algorithm based on a family of self - organizing feature map network according to the features of self - organizing feature map network. the algorithm is presented in detail in the paper. the simulated

    結合自組織神經網路的特點，提出自組織映射網路族的概念，在該概念的基礎上，提出一種新的基於自組織特徵映射網路的聚類演算法，詳細闡述了演算法步驟。
  19. In order to avoid matching the fault symptoms with the identification conditions artificially, ( fuzzy ) neural network was designed for diagnosis according to the optimal decision system. for the continuous quantitative diagnosis data such as the measurement, and the result of signal processing, a new hybrid system of self - organizing map ( som ) / fuzzy c - means ( fcm ), rough sets theory, and adaptive neuro - fuzzy inference system ( anfis ) was presented. firstly, the continuous attributes in diagnosis decision system were discretized with som or fcm

    對于連續的定量故障診斷數據（監測數據） ，以4135柴油機為例，提出了自組織映射（ som ）模糊c -均值（ fcm ） ?粗糙集?自適應模糊神經網路推理系統（ anfis ）集成的具體故障診斷實施方案：首先，應用som或fcm離散故障診斷數據中的連續屬性值；然後，基於粗糙集理論應用遺傳演算法計算診斷決策系統的約簡，按照實際需要確定診斷條件；最後，根據系統約簡設計anfis進行故障診斷。
  20. To accelerate the learning process of self - organizing mapping in the situation of large mount of data or high dimension, two learning algorithms were proposed in this paper, by using partial distortion search and extended partial distortion search respectively to solve the problem of nearest neighbor search during learning process, which could reduce the multiplications greatly

    摘要針對傳統的自組織映射網路在大數據量或高維情形下訓練過程較慢的問題，提出了分別使用部分失真搜索和擴展的部分失真搜索來完成傳統演算法中最耗時的最近鄰搜索過程，減少了完成訓練所需乘法次數。
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