遞歸神經網 的英文怎麼說

中文拼音 [guīshénjīngwǎng]
遞歸神經網 英文
rnn recurrent neural network
  • : Ⅰ動詞(傳送;傳遞) hand over; pass; give Ⅱ副詞(順著次序) in the proper order; successively
  • : Ⅰ動詞1 (返回) return; go back to 2 (還給; 歸還) return sth to; give back to 3 (趨向或集中於...
  • : Ⅰ名詞1 (神靈) god; deity; divinity 2 (精神; 精力) spirit; mind 3 (神氣; 神情) expression; l...
  • : 經動詞[紡織] (把紡好的紗或線梳整成經紗或經線) warp
  • : 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...
  • 神經 : nerve; nervus
  1. Neural network control is an important mode of intelligent control, and it is widely used in branches of control science, first, the architecture and the learning rule ( error back propagation algorithm ) of multiplayered neural network which is widely used in control system are presentedo especially, the paper refers to the architecture of diagonal recurrent neural network and its learning algorithm - - - - - recurrent prediction error algorithm because of its faster convergence with low computing costo next, before introducing the neural network control to the double close loop dc driver system, the controllers of current and velocity loop are designed using engineering design approach after analysis of the system, simulation models of the system are created

    路控制是智能控制的重要方式之一,它廣泛應用於自動控制學科各個領域。本文首先敘述了控制系統中常用的多層前饋路結構及演算法( bp演算法) ,特別提及了能夠較好描述系統動態性能的對角遞歸神經網路和在用推預報誤差演算法訓練drnn時取得了較快的收斂速度。其次,應用工程方法分析設計了tf - 1350糖分離機的電流、轉速雙閉環直流調速系統的控制器,作為引入路控制的設計基礎,並建立了系統的模擬模型。
  2. Simple recurrent neural network control for non - minimum phase nonlinear system

    非最小相位非線性系統的簡單遞歸神經網路控制
  3. A new recurrent neural network structure, self - feedback diagonal recurrent neural networks ( sdrnn ), is also designed in this chapter. the learning algorithm of sdrnn is given and the convergence of this algorithm is proved. the simulation results show the validation of the structure and the learning algorithm

    在局部遞歸神經網路結構方面,提出了一種遞歸神經網路結構? ?自環對角遞歸神經網路結構( sdrnn ) ,給出了相應的學習演算法,證明了演算法的收斂性,並進行了模擬實驗。
  4. The hybrid control of a permanent magnet linear synchronous motor ( pmlsm ) servo - drive system using an adaptive recurrent neural network was put forward to solve the problem of poor stable precision in the servo system of packaging binding machine

    摘要根據裹包機的驅動系統控制精度較差的問題,提出採用遞歸神經網路自適應混合控制線性同步電動器驅動機系統。
  5. Then, as the substitute of the traditional pi controller, neuron adaptive psd controller is used to alternate the velocity loop of dc driver system, simulation results show that the super - shoot in new system is small and the performance in the way of anti - disturbance and anti - time - varying parameters are higher than the traditional pi controller and robustness of the dc driver system improved greatly, in addition, through analysis of the character of the neuron adaptive psd controller, the paper presents a neural network self - tuning control method for dc driver system in which the diagonal recurrent neural network is used to identify the plant to calculate the plant ' s sensitivity for psd controllers simulation results indicate that excellent static dynamic target was got with this control method and the performance of the system is improved greatly,

    然後,應用單元自適應psd控制器改造調速系統的轉速環,代替傳統pi調節方式的轉速控制器。模擬表明,新系統在超調、抗負載擾動和參數時變方面性能優于的傳統的pi控制方式,系統的魯棒性增強。在分析單元自適應psd控制器特性后,本文提出用對角遞歸神經網路辯識控制對象,為psd控制器提供靈敏度參數,從而構成一路自校正控制方案。
  6. Evolutionary strategy based dynamic recursive neural network modeling and identification

    基於進化策略的動態遞歸神經網路建模與辨識
  7. Measurement of fouling in condenser based on diagonal recurrent neural network

    基於對角遞歸神經網路的冷凝器污臟測量
  8. So the paper combined the fuzzy logic control and recurrent neural network, and the recurrent fuzzy neural network ( rfnn ) controller is introduced into the speedsensorless vector control system. moreover, an online parameter training methodology, which is derived from the lyapunov stability theorem and gradient descent method is proposed to increase the learning capability of the rfnn. the rfnn controller has a better performance than the pi controller system ; the effectiveness of the proposed control scheme is verified by simulation results

    因此論文將模糊控制和具有優越動態性能的遞歸神經網路結合起來,取長補短,提出了一種模糊路控制方法,利用路來實現模糊推理,可動態的調整隸屬函數的形狀、位置以及權值,並對其與pi控制器的交流調速控制系統進行了模擬比較,模擬結果表明與普通的pi控制器相比較,模糊路控制器有較好的動態性能,控制器的收斂速度快、靜差小,系統在遇到參數發生變化和外部不確定性問題時魯棒性、抗擾動性有明顯的提高。
  9. According to the requirements to pd pattern auto - recognition, this paper studies systematically the basic theories and realizable methods for auto - recognition of pd gray intensity image : ( 1 ) in the requirement of on - line pd monitoring for transformer, several discharge models are designed and the relevant experiment methods projected. with discharge model tests, a lot of discharge sample data is acquired. on the base of systematical research on recognition for pd gray intensity image, this paper puts forward two kinds of fractal features, the 2nd generalized dimensions of original pd images and fractal dimensions of high gray intensity pd images, and then the relevant extraction methods

    針對局部放電模式自動識別的需要,作者系統地研究了局部放電灰度圖像自動識別中的基本理論和實現方法: ( 1 )根據變壓器局部放電在線監測的要求,設計了放電模型和實驗方法,並通過模型實驗獲得了大量放電樣本數據,為構造局部放電灰度圖像和採用bpnn進行識別作好準備; ( 2 )研究了局部放電灰度圖像的構造方法以及降維構造32 32灰度和矩陣的方法;在用人工路對局部放電進行模式識別時,分析了bp路的優缺點,對典型bp路的結構和學習訓練演算法提出了改進,採用帶有偏差單元的遞歸神經網路作為模式分類器;採用32 32灰度和矩陣進行bpnn識別結果表明這種方法是有效的。
  10. Active noise control using a diagonal recurrent neural network

    噪聲有源控制的遞歸神經網路方法
  11. Some achievements acquired are as the follows : ( 1 ) to meet the demands of the identification and control of nonlinear dynamic systems, the dynamic recurrent neural networks ( drnn ) are applied in this study, which integrate the nonlinear mapping abilities of the feedforward nn with the dynamic evolution capabilities of the feedback nn

    本文的研究成果包括: 1 .為滿足非線性動態系統辨識與控制的需要,本文結合了靜態前饋路與動態反饋路的優點,採用具有隱層自反饋的elman型動態遞歸神經網路( drnn )以實現基於路的彈性連桿機構振動主動控制。
  12. The fuzzy system is constructed through the artificial neural learning algorithm. third, the fault diagnosis using neural networks is discussed in this paper, especially the internal backward neural network with deviation elements. its model and learning algorithm are showed in detail

    其中詳細討論了帶有偏差單元的遞歸神經網路的模型和學習演算法,在此基礎上合成了模糊路系統,將模糊理論和計算原理相結合,使路藉助其大規模的并行分佈處理結構完成模糊診斷的推理過程。
  13. Soft sensing model of viscosity based on diagonal recurrent network

    基於對角遞歸神經網路的粘度軟測量模型
  14. ( 4 ) with the help of the experimental samples, a drnn identifier is trained off - line utilizing the compound identification method. the nonlinear dynamic model is achieved for the experimental mechanism

    4 .在系統辨識方面,本文以實驗輸入輸出數據作為訓練樣本,採用復合辨識方法離線設計了動態遞歸神經網路辨識器,獲得了彈性連桿機構的非線性動力學模型。
  15. ( 6 ) this dissertation makes a detailed study of the nn based adaptive control of nonlinear dynamic systems. two types of drnn controllers are designed using the experimental samples, which are named the open - loop controller and the close - loop controller respectively. the experimental mechanism is controlled on - line by means of the nn based direct self - tuning control strategy and the nn based indirect adaptive control strategy

    6 .本文對基於路的非線性動態系統自適應控制方法進行了深入研究,利用實驗輸入輸出數據離線設計了動態遞歸神經網路開環、閉環控制器,並分別採用基於路的直接自校正控制方法與基於路的間接自適應控制方法對彈性連桿機構實施了在線控制。
  16. 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

    根據動態校準實驗結果建立傳感器的動態數學模型,以研究傳感器的動態性能,是動態測試的一個重要內容.討論了遞歸神經網路模型在傳感器動態建模中的應用,給出了遞歸神經網路模型的結構及相應的訓練演算法.由於其反饋特徵,使得遞歸神經網路模型能獲取系統的動態響應.該方法特別適用於傳感器非線性動態建模,而且避免了傳感器模型階次的選擇的困難.試驗結果表明,應用遞歸神經網路對傳感器進行動態建模是一種行之有效的方法
  17. Considering self - recursion " structure can work on its inversion effect, so the paper uses contractive mapping genetic algorithm to search its optimal struct

    考慮到自遞歸神經網路的結構影響到其反演效果,本文利用壓縮映射遺傳演算法來搜索其最佳的結構。
  18. ( 4 ) the paper has studied the structure and training algorithm of self - recursion neural network. it also studies the convergence and stability of self - recursion neural network " training algorithm by using lyapunov method. then the paper use inversion method to simulate the vibration field of ground vibration caused by track traffic by self - recursion neural network for the first time

    ( 4 )研究了自遞歸神經網路的結構及訓練演算法,利用lyapunov方法研究了自遞歸神經網路訓練演算法的收斂性和穩定性,並首次利用自遞歸神經網路來反演軌道交通引起地面振動的振動場。
  19. Vibration fault diagnosis of hydroelectric generation sets based on spectrum analysis and irn network

    基於頻譜法和帶偏差單元遞歸神經網路的水電機組振動故障診斷
  20. 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

    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路本質上是一種靜態路,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。
分享友人