value n. 1.價(jià)值;重要性;益處。 2.估價(jià),評(píng)價(jià)。 3.價(jià)格,所值;交換力。 4.(郵票的)面值。 5.等值;值得花的代價(jià)。 6.(字等的)真義,意義。 7.【數(shù)學(xué)】值;【語(yǔ)言學(xué)】音值;【生物學(xué)】(分類上的)等級(jí);【音樂(lè)】音的長(zhǎng)短;【繪畫】明暗配合。 8.〔 pl.〕生活的理想,道德價(jià)值;社會(huì)準(zhǔn)則。 rated value 額定值。 proper value 【物理學(xué)】本征值。 commercial [economic] value 經(jīng)濟(jì)價(jià)值。 exchange(able) value (= value in exchange) 交換價(jià)值。 value in use 使用價(jià)值。 surplus value 剩余價(jià)值。 face value 票面價(jià)格。 market value市價(jià)。 pay full value for sth. 對(duì)某物付足代價(jià)。 the value of the dollar 美元的購(gòu)買力。 the value of a symbol 某符號(hào)的意義。 be of [no] value有[無(wú)]價(jià)值。 (for) value received 〔支票用語(yǔ)〕貸款…正。 of value有價(jià)值的 (news of value重要消息)。 out of value(繪畫等)明暗不調(diào)和。 place a value on 估價(jià),評(píng)價(jià)。 put [set] a high [much] value (up)on 高估;重視,看重。 vt. 1.給…估價(jià),定…的價(jià)。 2.對(duì)…作出評(píng)價(jià);尊重,看重(Troops are valued for quality rather than for number. 兵貴精不貴多)。 value oneself for (what one does, etc.) 夸耀(自己事業(yè)等)。 value oneself (up)on 自夸 (sth.) (value up(on) one's knowledge 夸耀自己的知識(shí))。
A method for ica for complex - valued sources 一種針對(duì)復(fù)值信號(hào)的獨(dú)立分量分析方法
A novel algorithm is proposed for training complex - valued neural networks 摘要提出了一種新型復(fù)數(shù)前饋神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)算法。
Based on the second order statistics , an algorithm is proposed to separate mixed complex - value signals online 摘要基于二階統(tǒng)計(jì)量,對(duì)在線分離復(fù)值混合信號(hào)法進(jìn)行了研究。
The full - rank matrix is employed to find the complex - valued weights between hidden and output layers by the least mean square algorithm 利用這個(gè)滿秩矩陣,通過(guò)最小平方算法就可以求得隱層和輸出層之間的復(fù)數(shù)權(quán)值。
To improve learning speed , a novel method for properly initializing the parameters ( weights ) of training complex - valued neural networks is proposed 摘要為了改善學(xué)習(xí)速率,提出了一種確定復(fù)數(shù)神經(jīng)網(wǎng)絡(luò)初始權(quán)值的新穎方法。
Because the initialized weights are optimized , the training accuracy and the learning speed are improved a lot for training complex - valued neural networks 初始權(quán)值的優(yōu)化,使得該算法可以有效地提高復(fù)數(shù)神經(jīng)網(wǎng)絡(luò)的訓(xùn)練速度和計(jì)算精度。
An online algorithm for complex independent component analysis was proposed based on the complex nonlinear functions and the decorrelation of two complex - valued vectors 摘要基于復(fù)向量不相關(guān)特性和復(fù)值非線性函數(shù),提出一種在線復(fù)值獨(dú)立分量分析算法。
The complex - valued weights between hidden and output layer are updated by solving linear system based on finding the complex - valued weights between input and hidden layer 當(dāng)輸入層和隱層之間的權(quán)值計(jì)算出來(lái)后,就可以通過(guò)求解線性方程組得到隱層和輸出層之間的權(quán)值。
The main idea is to make full use of the decorrelation of two complex - valued vectors in generating independent components by non linear decorrelation 結(jié)合非正則復(fù)向量的協(xié)方差矩陣和偽協(xié)方差矩陣構(gòu)造出了新的代價(jià)函數(shù),進(jìn)而提出新算法,通過(guò)復(fù)非線性不相關(guān),從混合信號(hào)中提取出復(fù)值獨(dú)立分量。
The gabor transform is one of the most important schemes for time - frequency analysis . since the traditional gabor transform is complex - valued , it ' s real - time applications were limited due to the high complexity involved in the computation of the complex - valued transform Gabor變換是重要的時(shí)頻分析方法之一,由于傳統(tǒng)gabor變換為復(fù)值變換,計(jì)算復(fù)雜度高、計(jì)算量大,限制了gabor變換的實(shí)時(shí)應(yīng)用。