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back propagation algorithm中文是什么意思

  • 倒傳遞演
  • 反傳算法
  • 反向傳播算法
  • 回傳式學習算法

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  • 例句與用法
  • In this paper , we improve the objective function of back propagation algorithm based on different financial actual situation . change the objective function into the expectation and the variance of error function to make its application more wide
    本文首先基于bp算法應用于金融實務領域的不同,對原bp算法的單一目標函數(shù)進行了改進,分別取其期望目標和方差目標,進行了bp算法的推廣,使其應用范圍更廣。
  • In order to override the well - known limitation of back propagation algorithm , such as local grade problem , we suggest genetic algorithm , a global optimization algorithm , to optimize the weights set . the different parts of this model were modularized and combined as a prediction system
    通過對固定網絡結構的權系值進行遺傳操作,優(yōu)化網絡的權系值組合,快速收斂到最優(yōu)權系值組合,進而提高網絡的分析預測效率和能力。
  • The multiplayer forward neural network and its training algorithm are thorough analyzed , error back propagation algorithm is derived from the mathematic , the problem of bp algorithm is indicated . the improved bp algorithm with many target recognition is constructed
    詳細分析了多層前饋型神經網絡描述及訓練算法機理,從數(shù)學的角度推導了誤差逆?zhèn)鞑ニ惴? bp算法) ,同時指出了bp算法存在的問題。構建了一種用于多目標識別的改進的bp算法。
  • 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
    在對基于誤差反向傳播學習算法的多層前向人工神經網絡進行分析基礎上,結合傳統(tǒng)自適應噪聲抵消系統(tǒng)基本原理,建立了基于人工神經網絡的自適應噪聲抵消器,經基于matlab的simulink仿真實例證明,具有很強的噪聲濾除能力和魯棒性。最后并提出了網絡及算法進一步改進的方法。
  • Based upon the deficiencies of the back propagation algorithm in the practical application , after some mechanisms effecting the network training and the other performances are analyzed when training samples with disturbance are employed in training , in this paper , through combining the chief thoughts of the classical bp algorithm and the robust statistic technique , improving the optimal algorithm of the bp algorithm , a new algorithm with high robustness - robust adaptive bp algorithm is proposed , and also make a good effect when integrated this new algorithm with the dynamical bp network to predict the stock price
    本文從基本bp算法在應用中存在的不足出發(fā),著重分析了訓練樣本中所含噪聲對基本bp算法在網絡訓練過程中產生的不良影響,并以此為依據,采用魯棒統(tǒng)計技術,同時在優(yōu)化算法上做了一些有益的改進,提出一種新的具有較強抗干擾能力的bp算法? ?魯棒自適應bp算法,并將其應用于動態(tài)bp網絡,進行股票價格的預測,取得了較好的預測效果。
  • 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(yè)生產過程操作優(yōu)化策略和應用實施方法,包括生產過程離線優(yōu)化策略、非線性問題求解策略、在線優(yōu)化模型及學習策略;結合揚州有機化工廠計算機集成制造系統(tǒng)集散控制系統(tǒng)( 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(yōu)化控制這一工程實際問題,采用統(tǒng)計建模方法,建立了聚合反應過程的優(yōu)化模型;為求解所得的優(yōu)化模型,提出了種改進的有約束條件下的模擬退火算法,該算法能避免陷于局部最優(yōu)解,有效地提高了所求解的全局性和可靠性:提出了基于改進的ga算法和sp算法相結合的混合學習算法,建立了基于神經網絡的聚合反應過程生產目標在線預測模型,該算法和模型滿足了生產中的實時性和實用性要求。
  • 5 based on studying artificial nerve network theory , establish the nerve network model of cutting parameter . use back propagation algorithm ( bp algorithm ) as system ' s nerve network learning method . system has realized cutting parameter nerve network intelligentized choosing function
    5在深入研究人工神經網絡理論的基礎上,建立了切削參數(shù)選擇的神經網絡模型,采用誤差反傳算法(即算法)作為切削參數(shù)智能選擇的神經網絡學習方法,實現(xiàn)了切削參數(shù)人工神經網絡智能選擇功能。
  • Using error back propagation algorithm . the limitation of the bp net is also improved ; 2 > when prediction with little training sample set and large variable size is concemed , this paper abstracts the prime factors from the training sample set using rough set theory , then only inputs the prime factors into ann . this diminishes the size and the input nodes number of ann
    對于即網絡本身易陷入局部極小點及收斂速度慢的問題,在文中也得到了改進; 2 、對于財政轉移支付中標準收入的測算屬于多指標小樣本的預測問題,文中首次利用粗集理論對初始的指標體系進行約簡,提取出關鍵性的因素作為神經網絡的輸入。
  • After researching the application of artificial neural network to fault diagnosis of transformer , the back propagation algorithm is improved observably . inducting chaos dynamics principium to create chaos neural network back propagation algorithm and using the pretreatment of training sample , the emulation program show that this method is effective
    本文深入研究了人工神經網絡在變壓器故障診斷中的應用,對目前最常采用的bp算法做出了較大改進,在原有算法中引入混沌動力學原理構造了混沌神經網絡bp算法,并提出了訓練樣本集的預處理方法,在仿真訓練中取得了滿意的效果。
  • On basis of such theory , penman apply back propagation algorithm to estimate a stock - sanxiashuili ( 600116 ) . compare the estimated price with the real price and analyze the reasons , we point out the advantages and the disadvantages of the new method finally and get some satisfying results
    在上述理論基礎上,筆者針對一支個股? ?三峽水利( 600116 )利用bp網絡建立了這支股票的估價模型,并將這支個股的估計價格和實際價格曲線進行了擬合,在實踐上也取得了較滿意的結果。
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