recurrent algorithm 中文意思是什麼
recurrent algorithm
解釋
遞歸演算法-
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。 -
However, recurrent kaufman formula used to compute call blocking probabilities ( cbps ) of virtual paths in “ step algorithm ” is time - consuming
然而，步進式演算法中用來計算虛路徑上的呼叫損失率的kaufman迭代公式非常的耗時。 -
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。 -
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。 -
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。 -
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。 -
An algorithm for radar emitter recognition based on internally recurrent net
基於回歸神經網路的雷達輻射源識別演算法 -
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 -
A dynamic nonlinear system is identified through recurrent nn using back propagation ( bp ) algorithm and dynamic bp algorithm respectively
然後利用遞歸網路對動態非線性系統進行辨識，並與傳統的預報誤差方法作了比較。 -
Trend prediction and fault diagnosis tech., etc. the information intelligent processing technology facing the application is presented as an emphasis. after introducing the development situation and the whole pattern on related fields, this paper describes several algorithm applied in the simulation experiment, including direct multi - steps nonlinear autoregressive - moving average ( narma ) prediction model based on diagonal recurrent neural networks and fuzzy neural networks model based on generalized probability sum ( gps ) and generalized probability product ( gpp ), and lists the algorithm steps facing the application
作為重點，本文辟用了較大的篇幅討論面向應用（主要是趨勢預測與故障診斷）的集成智能信息處理技術，在介紹相關領域的發展情況和總體格局之後，重點闡述了幾種基於神經網路的智能演算法，包括基於對角遞歸神經網路（ drnn ）的直接多步非線性自回歸滑動平均（ narma ）預測模型，以及基於廣義概率和（ gps ）與廣義概率積（ gpp ）兩種運算元的模糊神經網路模型，給出了它們的詳細演算法及面向應用的運算步驟。 -
2. the learning algorithm of recurrent neural network is investigated
2 .對遞歸神經網路的學習演算法進行了研究。
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