identification of the first kind 中文意思是什麼

identification of the first kind 解釋
第一類粘合
  • identification : n. 1. 認出，識別，鑒定，驗明（罪人正身等）。2. 【心理學】自居作用。3. 身分證。4. 【數學】黏合，同化。
  • of : OF ＝Old French 古法語。
  • the : 〈代表用法〉…那樣的東西，…那種東西。1 〈用單數普通名詞代表它的一類時（所謂代表的單數）〉 （a） 〈...
  • first : adj 1 最初的，最早的。2 最上等的，第一流的。3 基本的，概要的。4 高音（調）的。n 1 最初，第一；第...
  • kind : adj 1 厚道的，仁慈的，仁愛的；和藹的。2 親切的。3 〈古語〉相愛的，充滿柔情的。4 容易處理的；（毛...
  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. 3. the modeling method based on mechanism analysis and identification method always exits unmodeled high - order part and the modeling method based on neural networks usually has not good enough generalization capability. we fuse above two kinds of modeling method and put forward a hybrid modeling method based on mechanism analysis, identification and rbf neural networks. this paper proposed a hybr id modeling method based on mechanism analysis, identification and rbf neural networks. first, get a object ' s low - order model by the mechanism analysis and identification method. second, adopt rbf neural networks modeling method to compensate unmodeled high - order model. the sum of the low - order model and high - order model is the hybrid model. this kind of hybrid model has more accuracy than a model based on mechanism analysis and identification and has more generalization capability than a model based on neural networks

    針對基於機理分析和辨識的建模方法總是存在未建模的高階部分，精度不夠高和神經網路建模方法泛化能力差的缺點，將這兩種建模方法進行融合，提出基於機理分析、系統辨識和rbf神經網路的混合建模方法，首先採用機理分析和辨識的方法得到工業對象的低階模型，再用rbf神經網路建模方法補償未建模高階模型，這樣得到的混合模型，比單純基於機理分析和辨識的建模方法具有更高的精度，比單純的神經網路的建模方法具有更好的泛化能力。
  3. A project of authentication based on fingerprint feature data is studied : in this project, the first step is to sample the fingerprint of people and extraction its feature data through secugen hamster ( which is a kind of fingerprint sample device ), then embed the fingerprint feature data into the image ( both 256 gray - scale images and 24 bit color images ). during the identification course, match the fingerprint feature data sampling from the secugen hamster with that of picked

    研究了一種基於指紋特徵數據水印演算法的身份識別方案:結合secugen系列指紋儀，將採集的指紋圖像進行特徵提取，最後將指紋特徵信息嵌入到宿主圖像（ 256色或24位彩色圖像） 。在識別過程中，將採集的指紋信息和從圖像中提取的指紋特徵信息進行比對，如果比對成功，則可以確認其身份合法，否則，就是非法者。
  4. This paper focuses on the theories and controller designs of forward neural netwoks ? bp network. at first, the structure and algorithms of bp network are deeply researched, the ralations between momentum factor and convergence speed 、 convergence accuracy are revealed and a kind of improved bp algorithm is presented. then the identification method based on bp network with adaptive learning rate is studied and the simulaton indicates it can adaptively track the plant

    本課題主要針對前向神經網路? ? bp網路理論與控制器設計進行研究。首先重點對bp網路的結構和學習演算法進行了深入研究，揭示了動量因子與網路收斂速度、收斂精度之間的關系，並提出了一種改進的演算法。然後研究了採用自適應學習率bp網路的辨識方法，模擬說明其可以自適應地跟蹤辨識被控對象。
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