recurrent network 中文意思是什麼
recurrent network
解釋
鏈形電路-
The algorithm speeds up network study train, compared with the real - time recurrent learning algorithm consumedly, and increases the accuracy of prediction
與已有的實時循環學習演算法相比，極大的提高了網路學習訓練的速度，並且提高了預測的精度。 -
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
神經網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網路結構及演算法（ 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遞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，作為引入神經網路控制的設計基礎，並建立了系統的模擬模型。 -
Recurrent associative memory network
反饋型自聯想記憶神經網路 -
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 -
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。 -
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。 -
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，應用單神經元自適應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。在分析單神經元自適應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，從而構成一神經網路自校正控制方案。 -
For general nonlinear time series ( not - season time series ), on the foundation of pipelined recurrent neural network, bfgs ( broyden - fletcher - goldfard - shannon ) is introduced in, so a study algorithm based on bfgs is put forward
對於一般的非線性時間序列（非季節時間序列） ，我們在現有的管道神經網路基礎上，把bfgs演算法引入到該網路的學習中，提出了基於bfgs的管道神經網路學習演算法。 -
Research on multi - step prediction of deep excavation deformation based on recurrent neural network
神經網路的深基坑變形實時預報方法研究 -
A speed - sensorless control method for bldcm, which applies recurrent fuzzy neural network ( rfnn ), is presented in this paper based on the dynamic model of bldcm. the rfnn controller is used as a speed controller to mimic the optimized output of the system
本文基於bldcm的動態模型提出了一種性能較好的遞歸模糊神經網路（ rfnn ）無速度傳感器bldcm控制方法，採用rfnn控制器作為轉速控制器來近似最優控制器輸出。 -
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 -
The simulation results show the good performance for the system by using network to adjust the parameters and the recurrent weight of neural network on - line dynamically on the condition of variety of system parameter and the impact of outside uncertainty factors
模擬結果表明，當系統參數動態變化或受到外部不確定因素影響時，利用神經網路來在線調整網路的隸屬函數參數以及神經網路遞歸權值，使系統具有良好的動靜態性能。 -
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，提出了一種遞歸模糊神經網路控制方法，利用神經網路來實現模糊推理，可動態的調整隸屬函數的形狀、位置以及神經網路遞歸權值，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，遞歸模糊神經網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。 -
Active noise control using a diagonal recurrent neural network
噪聲有源控制的遞歸神經網路方法 -
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 -
The second is blind equalization algorithm based on bili near recurrent neural network. this algorithm adopt the bilinear recurrent neural network and design a new transmission function and cost function. through computer simulation, all proposed algorithm have better convergence performance
第二類是基於反饋神經網路（ rnn ）的盲均衡演算法，此類演算法使用了一種新型的雙線性反饋神經網路（ blrnn ） ，並將這種網路結構擴展到了復數域內，然後依據盲均衡的特點為網路設計了傳輸函數和代價函數。 -
The motor - drive loading system based on diagonal recurrent neural network
基於神經網路的三電平永磁同步電動機控制系統 -
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。 -
Routing algorithm based on two - layer recurrent neural network
基於雙層遞歸神經網路的路由優化演算法 -
Modeling and application based on diagonal recurrent neural network
基於對角遞歸神經網路的建模及應用
分享友人