中英
1
propagation algorithm中文是什么意思
- 中文翻譯
- 造句
- 繁衍算法
- algorithm n. 【數(shù)學(xué)】算法；規(guī)則系統(tǒng)；演段。
- back propagation algorithm 倒傳遞演; 反傳算法; 反向傳播算法; 回傳式學(xué)習(xí)算法
- bpa back propagation algorithm 反向傳播算法
- error back propagation algorithm 誤差反傳算法
- sequential propagation algorithm 順序繁衍算法
- propagation 持續(xù)培養(yǎng); 傳播，宣傳; 傳播；繁殖；生長(zhǎng); 傳波; 第八章繁殖技術(shù); 繁殖；傳送; 廣傳; 擴(kuò)散; 樂殖; 散括; 增長(zhǎng); 增生; 增殖
- the algorithm 算法
- abnormal propagation 不正常傳播; 反常傳播
- acoustic propagation 聲傳播; 聲傅播
- acoustical propagation 聲傅播
- angelfish propagation 神仙魚式傳輸; 鯊魚楔形傳輸
- animal propagation 動(dòng)物繁殖
- anogamic propagation 無配繁殖,無配生殖
- anomalous propagation 反差傳播; 反常傳播; 異常傳播
- antegrade propagation 前向性傳播
- antidromic propagation 逆向性傳播,逆行傳播
- artificial propagation 人工繁殖；人工放流; 人工放流
- asexual propagation 無性繁殖
- atmospheric propagation 大氣傳播
- back propagation 后向傳播，反向傳播
- bback propagation 反向傳播
- beam propagation 光束傳播
- bidirectional propagation 雙向傳播; 雙向傳導(dǎo)
- blast propagation 爆炸波傳播; 爆炸波的傳播
- propagation anomaly 傳播異常; 異常傳播損失
- propagation attenuation 傳播衰減
- 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(xì)分析了多層前饋型神經(jīng)網(wǎng)絡(luò)描述及訓(xùn)練算法機(jī)理，從數(shù)學(xué)的角度推導(dǎo)了誤差逆?zhèn)鞑ニ惴? bp算法) ，同時(shí)指出了bp算法存在的問題。構(gòu)建了一種用于多目標(biāo)識(shí)別的改進(jìn)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(duì)基于誤差反向傳播學(xué)習(xí)算法的多層前向人工神經(jīng)網(wǎng)絡(luò)進(jìn)行分析基礎(chǔ)上，結(jié)合傳統(tǒng)自適應(yīng)噪聲抵消系統(tǒng)基本原理，建立了基于人工神經(jīng)網(wǎng)絡(luò)的自適應(yīng)噪聲抵消器，經(jīng)基于matlab的simulink仿真實(shí)例證明，具有很強(qiáng)的噪聲濾除能力和魯棒性。最后并提出了網(wǎng)絡(luò)及算法進(jìn)一步改進(jìn)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(yīng)用中存在的不足出發(fā)，著重分析了訓(xùn)練樣本中所含噪聲對(duì)基本bp算法在網(wǎng)絡(luò)訓(xùn)練過程中產(chǎn)生的不良影響，并以此為依據(jù)，采用魯棒統(tǒng)計(jì)技術(shù)，同時(shí)在優(yōu)化算法上做了一些有益的改進(jìn)，提出一種新的具有較強(qiáng)抗干擾能力的bp算法? ?魯棒自適應(yīng)bp算法，并將其應(yīng)用于動(dòng)態(tài)bp網(wǎng)絡(luò)，進(jìn)行股票價(jià)格的預(yù)測(cè)，取得了較好的預(yù)測(cè)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(chǎn)過程操作優(yōu)化策略和應(yīng)用實(shí)施方法，包括生產(chǎn)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(xué)習(xí)策略；結(jié)合揚(yáng)州有機(jī)化工廠計(jì)算機(jī)集成制造系統(tǒng)集散控制系統(tǒng)( yh - cims dcs )的實(shí)施，針對(duì)錦綸? 6浸膠南京理工大學(xué)博士學(xué)位論文摘要簾于布生產(chǎn)中己內(nèi)酚胺聚合反應(yīng)過程優(yōu)化控制這一工程實(shí)際問題，采用統(tǒng)計(jì)建模方法，建立了聚合反應(yīng)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(jìn)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(jìn)的ga算法和sp算法相結(jié)合的混合學(xué)習(xí)算法，建立了基于神經(jīng)網(wǎng)絡(luò)的聚合反應(yīng)過程生產(chǎn)目標(biāo)在線預(yù)測(cè)模型，該算法和模型滿足了生產(chǎn)中的實(shí)時(shí)性和實(shí)用性要求。 - Feature extraction through 2 - order polynomial fit of the descending part of the response curve made possible a timesaving measurement process . the performances of two pattern recognition algorithms , namely principal component analysis ( pca ) and linear discriminant analysis ( lda ) in practical problems were discussed . artificial neural network ( ann ) was utilized with back - propagation algorithm ( bpa ) , and the combination of pca / lda with ann improved the identification performance of the system
基于對(duì)模式識(shí)別系統(tǒng)的深入研究，提出了從響應(yīng)階段數(shù)據(jù)提取特征的方法，節(jié)省了測(cè)試所需時(shí)間；比較了主成分分析法( principalcomponentanalysis , pca )與線性判別式法( lineardiscriminantanalysis , lda )兩種模式識(shí)別方法在實(shí)際應(yīng)用中的不同結(jié)果，分析了原因；設(shè)計(jì)了采用誤差反傳算法back - propagationalgorithm , bpa )的前向人工神經(jīng)網(wǎng)絡(luò)( artificialneuralnetwork , ann ) ，并指出其應(yīng)用中存在的問題，提出了改進(jìn)建議；利用pca lda與ann相結(jié)合的方法改善了系統(tǒng)的識(shí)別性能。 - As for turbine , the importance of its fault diagnosis is introduced ; the research status quo in this field all over the world and the difference from variable diagnosis method is presented . hybrid model fault diagnosis method based on expert system and back - propagation algorithm is applied to solve the turbine fault diagnosis . good effect is obtained
針對(duì)汽輪機(jī)故障診斷問題，本論文剖析了汽輪機(jī)故障診斷的重要性，論述了國(guó)內(nèi)外相關(guān)領(lǐng)域的研究現(xiàn)狀，分析了多種診斷方法的優(yōu)缺點(diǎn)，選擇了基于專家系統(tǒng)和基于反向傳播算法的混合模型的故障診斷方法來解決該問題，取得了很好的效果，全文的主要研究?jī)?nèi)容和成果總結(jié)如下： ( 1 )概述了一些現(xiàn)有的故障診斷方法。 - 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(jīng)網(wǎng)絡(luò)理論的基礎(chǔ)上，建立了切削參數(shù)選擇的神經(jīng)網(wǎng)絡(luò)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(jīng)網(wǎng)絡(luò)學(xué)習(xí)方法，實(shí)現(xiàn)了切削參數(shù)人工神經(jīng)網(wǎng)絡(luò)智能選擇功能。 - 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(qū)的方法:提出了一種應(yīng)用于半監(jiān)督bp算法的實(shí)用結(jié)束判據(jù)，并根據(jù)粗糙集理論，研究了一種新的粗糙分類機(jī)制，取得良好的效果；應(yīng)用支持向量機(jī)理論，構(gòu)造分類器并劃分樣本邊界區(qū)；最后研究多個(gè)分類器集成的方法尋找樣本邊界區(qū)，同樣提高了暫態(tài)穩(wěn)定評(píng)估的可靠性 - This dissertation is based on the application of ldpc codes in dvb - s2 standard . firstly , the dissertation reviews the traditional channel - coding schemes and the research about ldpc codes . secondly , it introduces the generation coding schemes of ldpc codes and the principle of belief - propagation algorithm
本文以dvb - s2標(biāo)準(zhǔn)中l(wèi)dpc碼的應(yīng)用為研究背景,首先介紹了信道編碼技術(shù)的發(fā)展及l(fā)dpc碼的研究現(xiàn)狀,然后詳細(xì)闡述了ldpc碼通用編碼方法和置信傳播譯碼算法基本原理。
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