feed-forward neural network 中文意思是什麼
feed-forward neural network
解釋
反向傳輸神經網路-
Feed - forward neural network based on gaussnewton - nl2sol algorithm and its application
法的前饋神經網路及應用 -
Wavelet neural network ( wnn ) is a novel feed - forward neural network based on wavelet theory, and it possesses several excellent features
摘要小波神經網路是建立在小波理論基礎上的一種新型前饋神經網路，具有許多優良特性。 -
Based on rbf neural network and perceptron neural network, a four - layer feed - forward neural network named radial basis perceptron ( rbp ) network is presented
基於rbf網路和感知器（ perceptron ）網路建立一四層前饋神經網路?徑向基感知器（ radialbasisperceptron ， rbp ）網路。 -
The feed - forward neural network is provided to recognizing the currency values, which makes use of the capability to extract features automatically and the error tolerance of neural networks
利用神經網路自動特徵提取能力和容錯特性，提出使用前饋神經網路對面值進行識別。 -
Firstly, studied feed - forward neural network and put forward a new algorithm on bp network, called bp algorithm based on robust error function ( bparef ), and the algorithm is proved to be effective for approaching nonlinear system
首先研究了前饋神經網路，提出了基於魯棒誤差函數的bp神經網路的演算法，並且驗證了其對非線性系統逼近的有效性。 -
In the last of this paper we apply our algorithms to the learning of feed - forward neural network, and get some new learning algorithms. we also give some numerical experiments to compare our algorithms with others
最後，將得到的這些優化加速收斂方法應用到了多層前饋神經網路的學習過程，給出了加速收斂的bp演算法，通過實際神經網路學習問題驗證了工作的成效。 -
Recently a covering method for feed - forward neural network design has been proposed by professor zhang ling. based on the sphere neighborhood model, this method transforms the design of neural classifiers to a geometrical covering problem
近來張鈴教授提出了一種前饋神經網路設計的覆蓋方法，它以球面領域模型為基礎，使得神經網路的設計轉化為幾何覆蓋問題。 -
To overcome the limitations of general fnns and bp algorithm, this thesis introduced a hybrid feed - forward neural network, which is composed of a linear model and a general multi - layer fnn, and proposed a new learning algorithm for the hybrid fnn
其次，針對bp網路存在的缺陷，結合前向神經網路和線性最小二乘法的優點，構造了一種基於混合結構的神經網路，提出了相應的非迭代的快速學習演算法。 -
Based on the analysis and processing of the digital speckle pattern, the translation and rotation invariant features are discovered, and a one - step feed - forward neural network is creatively proposed which makes it possible to realize the intelligent recognition of interface defects
通過對數學散斑條紋的分析與處理，找出了能代表條紋信息的移位不變與旋轉不變特徵值? ?最大斜率，進而構造一種新的網路模型，即單步前饋式三層網路系統.率先實現了把神經網路系統用在數字散斑無損檢測之中，完成了神經網路系統對粘接界面缺陷的智能辨識 -
The method using wavelet packet analysis is proposed to extract fault information from vibration signal obtained from testing jig of tilting train. the vector comprised of the energy of signal in all spectrum bands is input to a feed forward neural network
利用小波包分析，將擺式車體試驗臺上採集到的振動加速度信號分解在相互獨立的頻帶之p內，各頻帶內的能量值形成一個向量，將其作為神經網路的輸入特徵向量， 。 -
A three - tiered artificial neural network ( ann ) model with a feed - forward, back - propagation net - work structure was developed to forecast river flow in the manwan reservoir
摘要以雲南省漫灣水電站歷史徑流狀況為研究對象，運用三層前饋反向傳播神經網路模型對徑流進行中長期預報。 -
It adopted a bp neural network with a lag factor to identify the counter static model of process and decided parameters of feed - forward control through a model image, adopted a feedback controller that can adjust fuzzy logic according to the changes of object ' s parameters to improve dynamic control effect and adopted a control strategy simulating human intelligence to harmony works of system by switching among each control combination
它採用加滯后因子的bp神經網路辨識對象的靜態逆模型，並通過模型映射直接決定前饋控制的參數；用可隨對象參數變化調整模糊邏輯的閉環控制器配合前饋控制器以改善動態控制效果；用仿人智能控制策略協調系統的操作，在各種組合控制方式之間進行切換。 -
Later on, after elaborating the disadvantages of the old methods in detecting and recognizing moving objects, a series of corresponding approaches are proposed, such as grid scan, local tracking bug and dynamic window in object tracing to reduce the huge data needed to be processed, maximum and minimum for selecting a proper segmentation threshold and improved conversion from rgb model to hsv and so on to decrease the influence of inhomogeneous lighting and the color noise, a bilinear interpolation in each quadrant to eliminate the bad effect on the recognition precise because of the distortions of the camera. after that, much emphasis is given on application study in pattern recognition with a feed - forward neural network. both the basic bp algorithm and improved bp algorithm in the study process are described in detail, and the later is used to quicken convergence speed and improve validity of the network
然後，分析和闡明了傳統的運動目標檢測方法的不足，並在此基礎上結合研究中的實際實驗環境，提出了一系列解決方法，包括針對降低龐大數據量而提出的網格掃描、局部「跟蟲」追蹤和動態窗口掃描等目標檢測方法，針對實驗環境中光照不均和顏色干擾提出基於人機交互的最大最小值閾值選取方法和引入改進的rgb模型到hsv模型的轉換方法，為消除圖像畸變對識別精度的惡劣影響而採用的通過控制點進行雙線性插值進行畸變校正的方法；緊接著，概述了神經網路的發展歷史和幾種常用神經網路模型的特點，重點研究了前饋型神經網路在模式識別中的應用問題，詳細闡述了基本的bp演算法和學習過程中bp演算法的改進，從而使網路收斂速度更快，解決問題更有效，並在此基礎上，設計了一個基於bp神經網路的運動目標識別系統，給出了實驗結果。 -
As neural network has the ability of self - learning, that utilizes prior output data of uncertain system to estimate iteratively the static state property of system in order to achieve ideal approaching precision for identification of the positive model, a robust iterative learning control scheme on the basis of better positive model is designed. the neural network is used to identify the positive model of nonlinear system on iterative axis, which can give feed - forward action of iterative learning controller to reduce the effects of nonlinear properties and model uncertainties. meanwhile, feedback action of iterative learning controller make joint movement follow the desired trajectory on time axis by using controlled parameters derived by the neural network
由於神經網路具有自學習能力，它可利用不確定性系統的歷史輸出數據對系統的穩態特性進行估計，使得對系統正向模型的辨識達到理想的逼近精度，然後在此正向模型的基礎上進行學習控制律的設計：即採用神經網路辨識非線性系統的正向模型，並消除系統不確定性和外部干擾的影響，使關節運動沿迭代軸方向逼近期望軌跡；迭代學習控制器在線學習控制參量，使關節運動沿時間軸方向跟蹤期望軌跡。 -
The characteristic of method is, in every process of iterative learning, after obtaining better approaching precision of network training for model identification iteratively, the feed - forward action of iterative learning control law for the next trail is constructed by output signals of the neural network, and then integrated with feedback control to track the desired trajectory of robot in real time
該方法的特點是，在每一次迭代學習過程中，使神經網路訓練到對模型的辨識達到比較好的逼近精度后，利用神經元網路的輸出構造下一次迭代學習過程中控制律的前饋部分，再將它與實時反饋控制結合，形成本文提出的魯棒迭代學習控制演算法，並對機器人系統進行控制。 -
According to the experiences and data of the skilled operators, a feed - forward controller which is based on the neural network technology was added on the conventional cascade pid control system
文中根據電廠熟練運行操作員的實際操作經驗和數據，在常規串級pid控制系統的基礎上，增加設計了基於神經網路技術的前饋控制器。 -
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。 -
Secondly, this paper proposes a neural network training scheme based on the linear least - square method through the characteristic analysis of multi - layered feed forward neural networks, and then applies that to identify a pmsm model
接著通過對多層前向神經網路特點的分析，給出了一種基於線性最小二乘法的神經元網路訓練方案，並把它應用於永磁同步電機模型的辨識中。
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