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feedforward network in Chinese

Pronunciation:
How to pronounce "feedforward network""feedforward network" in a sentence

Translationmobile phoneMobile

  • 前饋網(wǎng)絡(luò)

Examples

  • The one main way that nn is applied to fdd is dynamic system identification with feedforward networks
    采用前饋型網(wǎng)絡(luò)對(duì)動(dòng)態(tài)系統(tǒng)辨識(shí)是神經(jīng)網(wǎng)絡(luò)應(yīng)用到故障檢測(cè)和診斷中的主要方法之一。
  • In this paper multilayer feedforward networks is used to design the nonlinear flight control systems , including the systems identification and control
    隨著社會(huì)的發(fā)展，網(wǎng)絡(luò)的廣泛應(yīng)用，人們對(duì)信息的需求量越來越大，需要保存更多的信息。
  • Feedforward networks use back propagation algorithm to train a multi - layer network . after training , the multi - layer network can fit the function in the data space very well
    前向網(wǎng)絡(luò)利用反向傳播算法訓(xùn)練多層網(wǎng)絡(luò)，使訓(xùn)練后的網(wǎng)絡(luò)較好地?cái)M合樣本空間中各點(diǎn)的函數(shù)值。
  • Three types of artificial neural networks , namely radial basis function network , feedforward network , stochastic network , and their application in the analysis of electromagnetic stop band structure in coplanar waveguide have been discussed
    本文研究了以下三種人工神經(jīng)網(wǎng)絡(luò)：徑向基函數(shù)網(wǎng)絡(luò)、前向網(wǎng)絡(luò)、隨機(jī)網(wǎng)絡(luò)，并用于分析共面波導(dǎo)中的電磁阻帶結(jié)構(gòu)。
  • The paper makes simulation studies into the field oriented control systems based on multiplayer feedforward networks and dynamic recursive networks respectively , and compares the convergence speed and approximate extent between the two learning algorithms
    對(duì)采用多層前向網(wǎng)絡(luò)和動(dòng)態(tài)遞歸網(wǎng)絡(luò)的矢量控制系統(tǒng)分別進(jìn)行了仿真實(shí)驗(yàn)，比較了兩種學(xué)習(xí)算法收斂的快速性和逼近程度。
  • In the paper , based on the existing literature research foundation an analog circuit catastrophic fault location approach by using feedforward networks with back ? propagation learning is realized . by this approach , the simulation require ments before test are reduced because fewer training samples are needed , and the fault location process is fast . this method is very efficient in location of single hard fault wit component tolerances . the measureme nt space feature and the general characterization concept of single and double soft fault in linear circuits are presented . according to this concept , a linear circuits soft fault location approach using subhidden layer bpnn is established with element tolerance , and it is shown that this approach is successful in fault location . a double fault feature extraction . ,
    本文在現(xiàn)有文獻(xiàn)理論研究的基礎(chǔ)上實(shí)現(xiàn)了采用bp算法前向多層神經(jīng)網(wǎng)絡(luò)對(duì)直流測(cè)試下模擬電路硬故障的診斷方法。其特點(diǎn)是采用少量典型特征樣本作為bp網(wǎng)絡(luò)的訓(xùn)練樣本,獲得訓(xùn)練樣本的代價(jià)小,減少了測(cè)前工作量,同時(shí)診斷速度快,在考慮元件容差時(shí)仍有好的診斷效果。文中介紹了線性電路單一軟故障和雙軟故障所具有的電壓增量空間特性和統(tǒng)一特征概念。
  • Nonlinear dynamic modelling of sensors is an important aspect in the field of instrument technique . the recursive neural network is proposed for nonlinear dynamic modelling of sensors , as its architecture is determined only by the number of nodes in the input , hidden and output layers . with the feedback behavior , the recursive neural network can catch up with the dynamic response of the system . the recursive neural network which involves dynamic elements and feedback connections has important capabilities that are not found in feedforward networks , such as the ability to store information for later use and higher predicting precision . a recursive prediction error algorithm which converges fast is applied to training the recursive neural network . experimental results show that the performance of the recursive neural network model conforms to the sensor to be modeled , and the method is not only effective but of high precision
    根據(jù)動(dòng)態(tài)校準(zhǔn)實(shí)驗(yàn)結(jié)果建立傳感器的動(dòng)態(tài)數(shù)學(xué)模型,以研究傳感器的動(dòng)態(tài)性能,是動(dòng)態(tài)測(cè)試的一個(gè)重要內(nèi)容.討論了遞歸神經(jīng)網(wǎng)絡(luò)模型在傳感器動(dòng)態(tài)建模中的應(yīng)用,給出了遞歸神經(jīng)網(wǎng)絡(luò)模型的結(jié)構(gòu)及相應(yīng)的訓(xùn)練算法.由于其反饋特征,使得遞歸神經(jīng)網(wǎng)絡(luò)模型能獲取系統(tǒng)的動(dòng)態(tài)響應(yīng).該方法特別適用于傳感器非線性動(dòng)態(tài)建模,而且避免了傳感器模型階次的選擇的困難.試驗(yàn)結(jié)果表明,應(yīng)用遞歸神經(jīng)網(wǎng)絡(luò)對(duì)傳感器進(jìn)行動(dòng)態(tài)建模是一種行之有效的方法
  • The paper makes research into the multiplayer feedforward networks and dynamic recursive networks , and proposes a method to estimate the speed and rotor flux of induction motors using the dynamic recursive networks . to the used dynamic recursive network model , the off - line dynamic bp algorithm has been reasoned out so as to observe induction motor state variables
    本文分別對(duì)多層前向網(wǎng)絡(luò)和動(dòng)態(tài)遞歸網(wǎng)絡(luò)進(jìn)行了研究，提出基于動(dòng)態(tài)遞歸網(wǎng)絡(luò)的異步電機(jī)的轉(zhuǎn)速估計(jì)和磁鏈觀測(cè)，針對(duì)采用的動(dòng)態(tài)神經(jīng)網(wǎng)絡(luò)模型，推導(dǎo)了離線動(dòng)態(tài)bp算法，以便利用動(dòng)態(tài)遞歸網(wǎng)絡(luò)進(jìn)行狀態(tài)觀測(cè)。
  • In this paper multilayer feedforward networks is used to design the nonlinear flight control systems , including the systems identification and control . the main contribution and creativeness result of this dissertation are summarized as follows : firstly , the basic theory of feedforward neural networks is reviewed . and this paper summarized the identification and control methods of nonlinear based on neural networks
    本文以某特定飛行器縱向運(yùn)動(dòng)非線性系統(tǒng)為被控對(duì)象，主要是針對(duì)系統(tǒng)中的非線性和未知因素，研究了前向神經(jīng)網(wǎng)絡(luò)( bp網(wǎng)絡(luò))在非線性飛行控制系統(tǒng)中的應(yīng)用，并進(jìn)行了大量的仿真驗(yàn)證，主要完成了以下工作： 1

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