back propagation 中文意思是什麼
back propagation
解釋
後向傳播，反向傳播- back : n 1 背，背部；背脊；背面，反面；背後，後部，後面，裏面。2 （指）甲；（刀）背；（手）背；（書）背...
- propagation : 持續培養
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Application of back propagation neural network to confect high strength premixing pumping concrete
神經網路在高強泵送混凝土配製中的應用研究 -
Firstly, second harmonic component ratio and dead angles of two phase inrush ' s dispersion in three - phase transformes are acted as input variable. secondly, the method applies improved algorithm based on the original algorithm of multi - layer forward back propagation network, that is to say, adding last variational effect of weight value and bias value to this time and making use of variable learning rate. at the same time, this method also adopts dynamic form in the number of hidden floor node
首先，文中將三相變壓器兩相涌流差流的二次諧波含量比和間斷角作為網路的輸入變量；其次，利用對原有bp網路訓練演算法基礎上的改進型演算法（即在計算本次權值和閾值的變化時增加上一次權值和閾值變化的影響以及採用變學習率，與此同時隱含層神經元個數採用動態形式） ，通過樣本訓練使網路結構模型達到最優。 -
Back - propagation approach to discriminative training of hidden markov model
隱馬爾可夫模型的一種有區分力的反向傳播訓練方法 -
Procreant knowledge expression and forward inference engine are adopted in the method of fault diagnosis based on expert system theory. in the fault diagnosis applying neural network theory, six kinds of improved arithmetic of back - propagation arithmetic, including gradient descent with momentum, variable learning rate back - propagation, resilient back - propagation, quasi - newton, levenberg - marquardt and conjugate gradient, are applied to diagnose the faults of electric load manage center and solid state power controller. different diagnostic results gotten by simulation are compared at last
在基於專家系統的故障診斷方法中，採用了產生式知識表達和正向推理機制；在基於神經網路的故障診斷方法中，則分別採用了bp神經網路的附加動量法、自適應學習速率、彈性bp演算法、擬牛頓法、共軛梯度法和levenberg - marquardt法對電氣負載管理中心和固態功率控制器的故障進行診斷，並對由模擬得到的不同診斷結果進行比較。 -
To achieve this goal, this paper design a neural network with three layers in which the first layer play a classifier role and learn with the memory - based learning algorithm while the second and third layers learn with the error back - propagation algorithm
根據這一需要，本文建立了三層神經網路，第一層起分類作用，採用基於記憶學習演算法，第二、三層採用誤差反饋學習演算法。 -
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。 -
二., the effection of back - propagation artificial neural network the ann model fitted very well in the study of typhoid fever and paratyphoid fever, viral hepatitis and epidemic cerebrospinal meningi - tis, yet, it did n ' t fit so well in the study of endemic typhus and en - demic encephalitis b. piscuss 1
在bp人工神經網路的預測效果方面，呼吸道傳染病百日咳和猩紅熱、消化道傳染病傷寒副傷寒和細菌性痢疾模型的預測效果較好。蟲媒傳染病的預測效果最差。在干早地區的傳染病預測方面， bp神經網路模型在回代和預測效果方面均優于傳統的多元逐步回歸模型。 -
Neural - fuzzy control driver ? model is made based on fuzzy logic and neural networks. fuzzy mapping of the control rule and triangular membership functions is carried out with bp ( back propagation ) neural networks
基於模糊神經網路理論，建立了模糊神經駕駛員控制模型，用bp網路實現了控制規則、隸屬度函數的模糊映射。 -
In this article, we applied our improved circular back - propagation ( icbp ) network to single step and multi - steps time series prediction respectively
在本文中將我們改進的圓形反向傳播網路模型（ improvedcircularbackpropagation - - icbp ）應用於時間序列預測，進行了單步和多步時間序列預測研究。 -
Icbp is a generalization to circular back - propagation ( cbp ) network. cbp possesses good generalization ability and adaptability compared with the counterpart bp. and in its frame we can construct vector quantification ( vq ) and radial basis function ( rbf ) networks, showing great flexibility
Icbp是我們對圓形反向傳播網路（ circularbackpropagation ? cbp ）的推廣， cbp不僅具有良好的推廣和自適應能力，而且在其框架下分別構建出矢量量化（ vq ）和rbf網路，展示出了極大的靈活性。 -
On the basis of analysing multilayer forward artificial neural networks which based on back propagation algorithms and basic principles of the adaptive noise cancellation system, this paper sets up an adaptive noise cancellation controller based on artificial neural network, which is proved to be more efficient in the noise cancellation and has robust performance based on simulink of matlab at the end, this paper proposes some advices of model and algorithms
在對基於誤差反向傳播學習演算法的多層前向人工神經網路進行分析基礎上，結合傳統自適應噪聲抵消系統基本原理，建立了基於人工神經網路的自適應噪聲抵消器，經基於matlab的simulink模擬實例證明，具有很強的噪聲濾除能力和魯棒性。最後並提出了網路及演算法進一步改進的方法。 -
The back - propagation ( bp ) network is one of the most useful artificial neural network ( ann ), which can deal with complicated data and nonlinear problem
Bp網路是最具有代表性的人工神經網路之一，具有強大的數據處理能力和非線性映射能力。 -
A particularly interesting item is the back - propagation network, a c package that illustrates a net that analyzes sunspot data
特別有趣，它是一個c程序包，說明了一個分析日斑數據的網路。 -
Forest volume estimate based on bayesian regularization back propagation neural network
神經網路估測森林蓄積量 -
Based on visual rice growth models ( vrgm ) and with rice tillering as examples, the back - propagation artificial neural networks ( bp ) and radial basis function networks ( rbf ) were established to simulate the rice growth and to compare with statistical model and dynamic model
3 、在研製可視水稻生長模型的基礎上，進一步利用人工神經網路bp模型和rbf模型，對水稻生長進行了模擬與模擬。並以水稻群體莖蘗動態為例，與水稻群體分蘗的統計模型和動力學模型進行了比較。 -
The computer simulation shows its advanteges of fast convergence and high approximation accuracy over the back propagation ( bp ) network and fuzzy neural networks
通過計算機模擬與bp網路和模糊神經網路進行了比較，發現收斂速度非常快，逼近誤差很小。 -
Seismic damage prediction for single - story reinforced concrete industrial building based on back propagation neural network
神經網路的單層鋼筋混凝土柱工業廠房震害預測 -
Based on the discussions of the conventional and recent methods of short term load forecasting such as time series, multiple regression approaches and artificial intelligence technologies, this paper presents a hybrid short term forecasting model which combines the artificial neural network ( ann ) and genetic algorithm ( ga ). in order to improve the convergence speed and precision of the back - propagation ( bp ), a new improved algorithm - the adapted learning algorithm based on quasi - newton method is given
本文首先分析比較了電力系統短期負荷預測的傳統方法時間序列法和回歸方法以及最近的專家系統和神經網路技術的優點和不足，然後針對人工神經網路bp演算法的不足對其進行了改進，採用了基於擬牛頓的自適應演算法，它提高了網路學習效率，具有較快的收斂速度和較高的精度。接著提出了改進的遺傳演算法來改善神經網路的局部收斂性。 -
Based on polymerization reaction of the nylon - 6 rubberized cord fabric production of distributed control system in yangzhou organic chemical plant computer integrated manufacturing system ( yh - cims / dcs ), the multiple stepwise regression method was used to build the statistic mathematical models of the molecule weight and the monomer quantum of casting slice belt. then the optimization model of polymerization reaction was presented, which was solved by using simulation annealing algorithm to obtain the best techniques parameters. the improved hybrid genetic algorithm and back propagation algorithm are combined to train neural network, brought out the neural network prediction model of casting slice belt ' s average molecule weight to guide the technologist on - line
提出了流程工業生產過程操作優化策略和應用實施方法，包括生產過程離線優化策略、非線性問題求解策略、在線優化模型及學習策略；結合揚州有機化工廠計算機集成製造系統集散控制系統（ yh - cims dcs ）的實施，針對錦綸? 6浸膠南京理工大學博士學位論文摘要簾于布生產中己內酚胺聚合反應過程優化控制這一工程實際問題，採用統計建模方法，建立了聚合反應過程的優化模型；為求解所得的優化模型，提出了種改進的有約束條件下的模擬退火演算法，該演算法能避免陷於局部最優解，有效地提高了所求解的全局性和可靠性：提出了基於改進的ga演算法和sp演算法相結合的混合學習演算法，建立了基於神經網路的聚合反應過程生產目標在線預測模型，該演算法和模型滿足了生產中的實時性和實用性要求。 -
This paper proposed some methods for finding out sure regions and ambiguous regions defined by lower and upper approximations in rough set theory. an applicable ending - criterion for semi - supervised back - propagation algorithm was proposed and a new rough classifier framework was studied, the assessment results show the effectiveness of the proposed criterion. a new classifier based on support vector machines was studied and applied
本文提出了幾種劃分樣本邊界區的方法:提出了一種應用於半監督bp演算法的實用結束判據，並根據粗糙集理論，研究了一種新的粗糙分類機制，取得良好的效果；應用支持向量機理論，構造分類器並劃分樣本邊界區；最後研究多個分類器集成的方法尋找樣本邊界區，同樣提高了暫態穩定評估的可靠性
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