weight n. 1.重量;體重;求心力,重力,(地心)引力。 2.斤兩,分量,衡,計(jì)重單位。 3.(壓東西的)重物。 4.砝碼,秤錘,秤砣。 5.重?fù)?dān),重壓;負(fù)擔(dān),重任。 6.重要性;影響力,勢(shì)力。 7.【統(tǒng)計(jì)】權(quán),加重值,權(quán)重,重要度。 8.(合于季節(jié)的衣料的)重量,厚薄。 9.拉弓所需的力。 10.【賽馬】馬應(yīng)負(fù)載的重量〔包括馬鞍、騎師等〕。 11.(拳師、摔跤手等的)體重級(jí)別。 a paper weight 鎮(zhèn)紙。 a pound weight 一磅重的秤砣[砝碼]。 a man of weight 重要人物,有勢(shì)力的人。 a suit of summer weight 夏服一套。 by weight 論重量(賣等)。 carry great [no] weight 極[不]重要,極受[不受]重視。 gain [lose] weight 體重增加[減少]。 give short weight 騙秤頭,克扣斤兩。 have weight with (sb.) 對(duì)(某人)有影響。 pull one's weight 盡自己的本分〔見(jiàn) pull 條〕 put on weight 體重增加,發(fā)胖。 throw one's weight about 大擺架子;作威作福,仗勢(shì)欺人。 under the weight of 在…的重壓下,迫于…。 weight empty 空重,皮重;空載。 weights and measures 度量衡。 vt. 1.在…上加重量,把重量放在…上。 2.使負(fù)擔(dān),裝載;使負(fù)重?fù)?dān),裝載過(guò)重;折磨,壓迫。 3.在(織品、絲等內(nèi))攙重晶石(等)以增加分量。 4.【統(tǒng)計(jì)】使加權(quán),附加加重值于。 5.視…為重要,強(qiáng)調(diào)。 weighted with 因…加重;因…煩惱。 adj. -less 無(wú)重的;失重的。 n. -lessness 失重。
Application of k - l transformation the optimization of initial weights of bp neural network 網(wǎng)絡(luò)初始權(quán)值優(yōu)化中的應(yīng)用
Nntcs employs genetic algorithm ( ga ) in the stage of training to optimize initial weights of ann 訓(xùn)練過(guò)程中結(jié)合遺傳算法,優(yōu)化神經(jīng)網(wǎng)絡(luò)的初始權(quán)值。
Genetic algorithm is used to optimize the initial weight of back propagation network and the operation efficiency is enhanced 用遺傳算法優(yōu)化bp網(wǎng)絡(luò)的初始權(quán)值,提高神經(jīng)網(wǎng)絡(luò)的運(yùn)算速度。
The initial weights of the neural network can be given according to the material meaning , which expedites the network convergence 文中將神經(jīng)網(wǎng)絡(luò)與ip控制器結(jié)合,權(quán)的初始值可據(jù)其意義設(shè)定,大大加快了網(wǎng)絡(luò)的收斂速度。
In the control process uses two bp network . one is used as nni recognizing the model , another as neural network control device ( nnc ) . but first off - line recognizes controlled device , make sure nnc initial weights 在控制的過(guò)程中,采用兩個(gè)bp網(wǎng)絡(luò),一個(gè)作為神經(jīng)網(wǎng)絡(luò)辨識(shí)器( nni )進(jìn)行辨識(shí)建模;另一個(gè)作為神經(jīng)網(wǎng)絡(luò)控制器( nnc ) 。
The algorithms for training weights update and constructing the target vectors are discussed . use the penalty term to improve the astringency of network . and study how choice the appropriate initial weights 著重研究了根據(jù)輸入和輸出量合理選擇網(wǎng)絡(luò)結(jié)構(gòu),訓(xùn)練權(quán)值的更新算法,目標(biāo)向量的合理構(gòu)造,帶懲罰項(xiàng)的bp網(wǎng)絡(luò),改善了網(wǎng)絡(luò)的收斂性。
The dependences in multitemporal multispectral images by independent component analysis are reduced . in the algorithm , damped factor is imported to reduce the dependence on initial weights , thus the robust of the algorithm is improved 在改進(jìn)的獨(dú)立成分學(xué)習(xí)算法中,通過(guò)在梯度下降方法中引入阻尼因子,降低了對(duì)初始值的依賴,提高了獨(dú)立成分求解的穩(wěn)健性。
During the course of develop fault diagnostic method , the influence to the training circle number with network structure 、 learning rate 、 initial weight value & door value etc are discussed . by comprehensive analyses and comparing , the comparatively rational value is adopted to be network ' s eigenvalue 在制粉系統(tǒng)故障診斷樣本訓(xùn)練過(guò)程中,本文作者探討了網(wǎng)絡(luò)結(jié)構(gòu)、學(xué)習(xí)率、初始權(quán)值閾值等因素對(duì)訓(xùn)練速度的影響,為選取合理的網(wǎng)絡(luò)參數(shù)提供了依據(jù)。