時延神經網路 的英文怎麼說

中文拼音 [shíyánshénjīngwǎnglù]
時延神經網路 英文
tdnn time delay neural network
  • 時 : shí］Ⅰ名1 （比較長的一段時間）time; times; days:當時at that time; in those days; 古時 ancient tim...
  • 神 : Ⅰ名詞1 （神靈） god; deity; divinity 2 （精神; 精力） spirit; mind 3 （神氣; 神情） expression; l...
  • 經 : 經動詞[紡織] （把紡好的紗或線梳整成經紗或經線） warp
  • 網 : Ⅰ名詞1 （捕魚捉鳥的器具） net 2 （像網的東西） thing which looks like a net 3 （像網一樣的組織或...
  • 路 : 1 （道路） road; way; path 2 （路程） journey; distance 3 （途徑; 門路） way; means 4 （條理） se...
  • 神經 : nerve; nervus
  • 網路 : 1. [電學] network; electric network2. （網） meshwork; system; graph （指一維復形）; mesh
  1. It lays foundation for further study of identification of material properties and it is significant for further study of sheet forming intellectualization

    為了擴延識別范圍，本文對rbf神經網路實時識別進行了研究。這對以後進一步研究板材成形智能化具有重要意義。
  2. This paper is focused on estimating the domains of attraction of equilibrium points for hopfield neural networks with delays and stabilizing a class of time - delays neural networks via standard feedback control

    本學位論文主要研究了時延hopfield神經網路平衡點吸引域估計以及通過標準反饋控制使一類時延神經網路穩定化。
  3. Chapter 4 discusses how to stabilize a class of time - delays neural networks via standard feedback control ; this kind of neural networks provides a unified view of several well - known neural networks ( such as hopfield neural networks and cellular neural networks ) with discrete delays or distributed delays. a stability criterion is given by using the lyapunov method. all the results obtained in this chapter are stated in simple algebraic forms so that their verifications and applications are straightforward and convenient

    通過使用lyapunov方法，我們得到了一類時延神經網路經過標準反饋控制后的穩定條件，這類神經網路涵蓋了幾種著名的神經網路，如hopfield神經網路、細胞神經網路，同樣，本章所得到的結論也是簡單的代數表達式，使用非常方便，最後在用計算機通過做數值實驗對這些結論進行了驗證。
  4. Chapter 3 investigates the estimation of the domains of attraction of equilibrium points for hopfield neural networks with delays, where hopfield neural networks with discrete and distributed delays are considered. we derive a sufficient condition for an equilibrium point to be locally exponentially stable

    分別針對帶離散時間延遲和帶分散式時間延遲的hopfield神經網路，得出了平衡點局部指數穩定的充分條件，同時提出了一種指數穩定平衡點的吸引域的估計方法。
  5. At last, compared the three data extention technologys merits. the time - series based and neuron network based data extention methods were proposed. chapter four studied the improvement of the emd algorithm al efficiency and precision. reserthed the effect of spline interpolation ' s end condition to the emd algorithm ' s precision, then discussed the low - order and propoed high - order spline interpolation based emd algorithm and their effects

    第三章介紹了emd演算法端點效應的機理；然後系統地研究了直接信號序列延拓技術、基於時間序列預測和基於神經網路預測的數據序列延拓技術的特點及性能；最後，對各種延拓技術進行了比較研究，分析了各種延拓技術的優缺點。
  6. Our condition and estimate are formulated in terms of the network parameters, the neurons ’ activation functions and the associated equilibrium point. hence, they are easily checkable. it is believed that these results are significant and useful for the design and applications of the delayed hopfield neural networks

    這些條件和估計的公式是由網路參數、神經元激活函數以及相應的平衡點構成，所以它們很容易使用，相信這些結果對于帶時間延遲的hopfield神經網路的設計和應用具有一定的重要性和使用價值。
  7. Dpcnn - delay pulse coupled neural network

    在時延脈沖耦合神經網路
  8. White noise acting as inspirit signal, the experiment data is collected. utilizing these data and error back propagating identification method, different neuron and input - output delay are selected. by comparing approximation ability and generalization ability, the neural networks model in position mode and velocity mode is identified

    並以模擬白噪聲為激勵信號，收集了數據;利用實際收集的數據，採用誤差反傳的辨識方法，選擇不同神經元及輸入輸出延時量的模型，通過比較網路的逼近能力和泛化能力，辨識得到了位置和速度兩種工作模式下的方位通道的神經網路模型。
  9. The paper proposes a novel learning algorithm for precise phase control of oscillatory neural networks. a target oscillator is employed to temporally synchronize correlated objects in an input binary image while uncorrelated objects are desynchronized via adjustable phase shift that is proportional to their spatial position. the proposed learning rule has a fast convergence, and can achieve an extremely small phase error

    設計了一種新的神經元振蕩器網路快速同步學習演算法，該演算法能保證同組的振蕩器達到完全同步；同時，採用相位延遲方法，使不同圖像目標的振蕩器組的相位延遲正比于其在空間上的距離，實現目標對象的分離與提取
  10. First, calculating time delay and embedding dimension to reconstruct phase space. second, based on chaos theories, the artificial neural network is used to build one - step and multi - steps predictive model. third, combining with an adaptive filter, predictive error is processed so that weak signal is extracted from strong chaotic noise

    研究的內容分為三個方面: （ 1 ）確定嵌入維數和延遲時間重構相空間; （ 2 ）將混沌理論與人工神經網路結合，建立混沌時間序列（混沌背景）的一步與多步預測模型; （ 3 ）結合自適應信號分離器對預測誤差進行處理達到檢測微弱信號的目的。
  11. Neural networks can be viewed as a universal approximator for nonlinear functions, but the multi - layer feed - forward neural network which be used usually is a static state network in nature, it is disagree with the real - time identification for dynamic system. moreover, recurrent neural networks can simulate the state memory mechanism of dynamic system, so it can be utilized as the model of dynamic time delay system

    神經網路具有逼近任意連續非線性函數的能力，但常用的多層前饋式反傳網路本質上是一種靜態網路，不適合動態系統的實時辨識，而遞歸神經網路能夠實現對動態系統狀態記憶機制的模擬，因此更適合於作為動態時延系統的模型。
  12. In this paper, we analyze the statistic characters of time - delay in detail, and put forward to adopt elman nn to forecast time - delay intelligently, design appropriate compensator based on adaptive smith forecast theory. the experiment results prove that this method can improve the forecast accuracy and system ' s dynamic performance. in the application of real time data transmission, forward error correction ( fec ) is a good method to resume the lost data

    針對這個問題本文進行了詳盡的統計特性分析，並且提出採用elman動態神經網路結構對網路時延進行智能預測，並根據自適應smith預估補償控制原理設計合適的時延補償器，並且應用於基於web的二階水位控制系統中，實驗結果證明了採用此方法可以提高預測精度，使時延補償器適應時延變化的動態性更強，從而在保證系統穩定性的基礎上，西安理工大學碩士學位論文提高系統的時延補償精度。
  13. For power control of listening users, forward power control method are introduced based on full, multichannel, filtered report, and collision method etc. the full report method has redundancy report information, and its real - time performance is bad, multichannel report is introduced to improve the real - time performance, and filtered report is introduced to eliminate the redundancy information, finally the collision method introduced can not only get higher real - time performance but also diminish the redundancy information ; 2. in order to meet the requirements of making the dynamic simulation of trunking group system, the ms ’ s random move equation is brought forward, the simulation of ms ’ s distribution is done and the integrated channel model are presented ; 3. the smart predicative model of power control is introduced to overcome the delay and track the change of the complicated network, with this model, the power control ‘ s performance is greatly improved

    全匯報方法存在冗餘的匯報信息，而且實時性較差，為了改善實時性提出了多通道的匯報方式，為了改善冗餘匯報而提出了篩選法，最後介紹的碰撞法在減少冗餘信息的同時又提高了實時性;二、為了集群功率控制動態模擬的需要，提出了移動臺的隨機運動方程，進行了有關移動臺的分佈模擬，建立了綜合的通道模型;三、希望克服延時和跟蹤復雜網路環境變化，提出了功率控制的智能預測模型，通過智能預測模型可以改善功率控制的性能，著重介紹了採用神經網路的方法實現智能預測的通用模型，從而跟蹤復雜多變的無線環境，諸如慢衰落及快衰落（包括多徑衰落、多普勒效應所引起的衰落）等網路特徵，達到預測功率需求;四、採用二級正交碼和智能天線（空分多址）的方法進行組內用戶的識別，改進功率控制效果;五、話權用戶的前向和反向功率控制方法;六、對引入gota的cdma系統提出了復合容量表示方法，並作容量分析，探討有關gota系統的qos問題。
  14. A sufficient condition guaranteeing the global asymptotical stability of the equilibrium point is derived for a class of neural network models with variable delay and neutral type delay

    摘要針對一類能夠由中立型變延遲非線性微分方程描述的神經網路模型，給出了全局漸近穩定的不依賴于時間延遲的充分條件。
  15. By the second method, neural network breakout prediction model are becoming popular and replacing previous logical decision ones in the world. this is due to the drawbacks of the logical decision breakout prediction, such as lack of robustness and error tolerability. in contrast to that, neural network has good self - learning ability, robustness, and can constantly increase the system performance with the increase of running time

    在第二條途徑上，用神經網路漏鋼預報系統替代早期的邏輯判斷預報系統在國際上已成為一種趨勢，這是因為邏輯判斷系統存在自身無法克服的缺點，如容錯性差，缺乏魯棒性等，而神經元網路具有較好的自適應性能力、魯棒性和容錯能力，並隨著使用時間的延長，不斷提高系統的性能。
  16. The delay characteristic on network is analyzed. a two - degree robot is used as the control object. the technology of neural networks for identifying the plant, general predictive control and data buffering logic are used to realize remote controlling

    分析了在internet網路環境下控制系統中的時延特性，以一個兩關節機械手為對象，採用神經網路進行對象特性的預測，通過廣義預測控制和信息緩沖技術實現遠程控制。
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