無監督學習 的英文怎麼說

中文拼音 [wújiāndūxuéxí]
無監督學習 英文
unsupervised learning
  • 無 : 無Ⅰ動詞（沒有） not have; there is not; be without Ⅱ名詞1 （沒有） nothing; nil 2 （姓氏） a surn...
  • 監 : 監名詞1. （古代官府名） an imperial office 2. （姓氏） a surname
  • 督 : Ⅰ動詞（監督指揮） superintend and direct Ⅱ名詞（姓氏） a surname
  • 學 : Ⅰ動詞1 （學習） study; learn 2 （模仿） imitate; mimic Ⅱ名詞1 （學問） learning; knowledge 2 （學...
  • 監督 : 1 （察看並督促） supervise; superintend; control; monitoring; supervision 2 （監督人） supervisor...
  1. 1, q 3, and at last prove the exisitence of ( q, m + n, n, m ) resilient functions when n > q ? 1. intelligentized ids methods, which can make the system more adaptability and self - studying, are important research directions of ids so far. in order to make the ids systems have better identifying ability and efficiency against new intrusions, we propose the intrusion feature extra - ction algorithm based on ikpca by studying the different kinds of intrusion detection feature extraction algorithm based on unsupervised learning, and then theoretically analysis the conver - gence of the algorithm. in addition, we validate the validity of the algorithm by means of experim - ents ; at the same time, through studying ica and neural networks, we propose fastica - nn ids, and then test the kddcup99 10 % date set to make comparison of kpca 、 ikpca and fastica algorithms in intrusion detection advantages and disadvantages

    為了使入侵檢測系統對新的入侵行為有更好的識別能力和識別效率，本文在研究了各種基於無監督學習的入侵檢測特徵提取方法的基礎上，提出了基於增量核主成份分析（ ikpca ）的入侵檢測特徵提取方法，並對該方法進行了收斂性分析，同時結合模擬試驗對其正確性進行了驗證;另外，本文通過研究獨立成份分析和神經網路，提出了基於快速獨立成份分析和神經網路的入侵檢測方法（ fastica - nnids ） ，並通過對kddcup99的10 %數據集的檢測比較了核主成份分析（ kpca ） 、增量核主成份分析（ ikpca ）和快速獨立成份分析（ fastica ）在入侵檢測特徵提取方面的優缺點。
  2. The former belongs to supervised learning and the latter belongs to unsupervised learning

    它們分屬于有監督學習與無監督學習。
  3. Due to its unsupervised learning ability, clustering has been widely used in numerous applications, such as pattern recognition, image processing, market research and so on

    聚類具有無監督學習能力，被廣泛應用於多個領域中，如模式識別、數據分析、圖像處理以及市場調研等。
  4. Approaches of immune computing, namely the aine model for unsupervised learning, airs model for supervised learning and the improved model of negative selection algorithm are exploited in an integrated way

    綜合運用aine無監督學習模型、 airs有監督學習模型和文中給出的陰性選擇演算法改進模型，提出了基於免疫計算的機構軌跡綜合方法。
  5. The supervised and unsupervised learning diagnosis methods are discussed and several improvements have been presented in the learning algorithms. the simulation results show that the proposed method can perforfti correct diagtioals iii the linear analog circuits with tolerances

    本文對模擬故障診斷的有監督學習和無監督學習方法分別進行了研究，通過對實現過程的分析，對經典的學習演算法進行深入研究，並提出若干改進。
  6. The main work includes : the research and conments about some recognition methods ; the research and comments about three kind of mathematics morphologic arithmetic ; clustering ; matlab embedded in the vb ; the difference analysis in the dynamic image and so on

    主要做的工作包括幾種識別方法的研究與評述；三種數學形態學演算法的實現以及各自的優劣比較；利用無監督學習進行聚類以及matlab在圖像處理中的嵌入；動態圖像的差分分析等。
  7. In the respect of neural networks control for non - linear and uncertain system, a review of some available control strategy is made. combining neural networks control and conventional control strategy supervised learning, no supervised learning and reinforcement learning neural networks self - studying and adaptive control systems for ship course control are proposed. the thesis studies particularly their characteristics

    在非線性和不確定性系統的神經網路控制方面，論文總結了一些現有的神經網路自學習控制系統，然後將神經網路和常規控制（例如pid控制、自適應控制、內模控制等）結合起來，根據船舶操縱的特點，詳細研究和分析了有監督學習、無監督學習和再勵學習的船舶航向神經網路自學習型自適應控制系統。
  8. The application of clustering into information filtering, to a certain degree, promotes the filtering efficiency of the system, and plays an active role in the examination of the precision and recall of the text. the indeterminacy and vagueness of natural language cause difficulty to nlp

    本質上，聚類屬於一種無監督的學習，將聚類技術應用於信息過濾中可以在一定程度上提高系統的過濾效率，同時也對信息過濾的查準率與查全率有積極的作用。
  9. This paper aims to combine advantages of pid control and neuron, propose the neuron pid controller which is derived from an incomplete derivative pid algorithm and based on six learning rules in common use, viz. no surpervized hebbian learning rule, perceptron learning rule, supervized learning rule, improved hebbian learing rule, delta learning rule and capability index which is based on second type, and these rules come into being six control arithmatic. then simulate in object with lag

    本論文主要將兩者的優點結合，提出了神經元實現不完全微分pid ，並採用神經網路常用的六種學習規則，即無監督hebb學習規則、感知器的學習規則、有監督的hebb學習規則、改進的hebb學習規則、 delta學習規則和基於二次型性能指標的學習規則，形成六種控制演算法，以工業生產過程中常見的二階純滯后對象為例進行模擬。
  10. By means of the proposed reinforcement learning algorithm and modified genetic algorithm, neural network controller whose weights are optimized could generate time series small perturbation signals to convert chaotic oscillations of chaotic systems into desired regular ones. the computer simulations on controlling henon map and logistic chaotic system have demonstrated the capacity of the presented strategy by suppressing lower periodic orbits such as period - 1 and period - 2. meanwhile, the periodic control methodology is utilized, the higher periods such as period - 4 can also be successfully directed to expected periodic orbits

    該控制方法無需了解系統的動態特性和精確的數學模型，也不需監督學習所要求的訓練數據，通過增強學習訓練方式，採用改進遺傳演算法優化神經網路權系數，使之成為混沌控制器，便可產生控制混沌系統的時間序列小擾動信號，模擬實驗結果表明它不僅能有效鎮定混沌周期1 、 2等低周期軌道，而且在周期控制技術基礎上，也可成功將高周期混沌軌道（如周期4軌道）變成期望周期行為。
  11. In this paper the method of inverse system and ath - order inverse system are introduced, then it studies the application of the method of inverse system and supervised neural controller in the excitation controlling of one machine infinite bus power system. after introducing the method of ann a th - order inverse system it studies its application in the excitation controlling of one machine system. at last as t the emphasis of this paper, combining the theory of multi - machine decentralized and coordinated control, the method of ann ath - order inverse system is studied how to be used in the excitation control of multi - machine power system

    本文首先介紹了逆系統和階積分逆系統理論，接著研究了逆系統方法和神經網路監督學習控制理論在單機?無窮大系統勵磁控制的綜合應用；然後在介紹了神經網路階逆系統控制理論后，研究了神經網路逆系統方法在單機?無窮大系統勵磁控制中的應用；最後作為本文研究重點，首次將神經網路逆系統方法應用於多機電力系統穩定控制，研究了神經網路逆系統方法在多機系統勵磁控制中的應用情況，並結合多機系統最優分散協調控制理論，設計出基於神經網路逆系統方法的多機電力系統分散協調勵磁控制器，進行了控制系統模擬和效果分析。
  12. The other is that when the extending areas of the samples overcross, wrong classification of the samples will occur. as for the first problem a genetic algorithm is used to improve the process of the best parameters " finding. and as for the latter a kind of improved hamming net which uses supervised and unsupervised learning method is employed

    針對模糊hamming網路在應用中存在的參數調整效率低下以及難以保證參數最優的問題，提出了應用遺傳演算法進行參數調整的改進方法；針對該網路在樣本離散范圍發生交疊情況下導致歸類錯誤的問題，研究了對于不同模式採用不同的警戒參數的有監督無監督混合學習的改進演算法。
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