GG asked in 社會與文化語言 · 1 decade ago

專業英文 英翻中 ~20點~

Since these methods have the nonlinear function

such as a sigmoidal function as the unit function

and some intermediate layers, they can easily construct

a nonlinear mapping function within the NN

and have the generalization ability to respond correctly

to inputs it has not specifically been trained for.

It should be noted, however, that they are easy to fall

into a local minimum, because the back-propagation

algorithm is basically a gradient method. In addition,

the network scale will become very large for a

multi input-output system such as the control problem

of robot manipulators; in particular, the number

of units in the intermediate layers increases [17] and

hence the learning process is not implemented effectively.

A method [18] is recently proposed by adjusting

the unit functions and the connection weights,

or only the unit functions, instead of adjusting the

only connection weights as done in the conventional

method. It is shown in [19],[20] that this methodgives

a faster learning process and less number of unit functions

in the intermediate layers than the conventional

met hod.

2 Answers

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  • 1 decade ago
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    由於這些方法有非線性函數

    如一個反函數為單位的功能

    和一些中間層,他們可以輕鬆地興建

    非線性映射功能與神經網絡並有泛化能力

    ,以正確回應投入卻沒有專門的訓練。

    應當指出,然而,它們很容易下降

    成為當地的最低,因為反向傳播

    算法基本上是一個梯度法。

    此外, 網絡規模將變得非常大,為一

    多輸入輸出系統等的控制問題機器人;

    特別是,有多少單位在中間層增加[ 17 ]和

    因此,學習過程是不是有效的實施。

    一種方法[ 18 ]是最近提出的調整該單位的功能

    和連接權值, 或只是單位的職能,而不是調整

    只有連接權所做的那樣,在常規方法。

    這表明在[ 19 ] , [ 20 ] ,這methodgives

    更快的學習過程和人數較少的單位的職能

    在中間層比常規會見部門首長。

    Source(s): 網站
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  • 1 decade ago

    因為這些方法有非線性作用譬如一個乙狀結腸的作用作為單位作用和一些中間層數, 他們能容易地修建一個非線性映射函數在NN 之內和有它不具體地被訓練了為的概念化能力正確地反應輸入。它應該是著名, 然而, 他們容易分成一個地方極小值, 因為傳播算法基本上是梯度方法。另外, 網路標度將變得非常大為一個多輸入- 輸出系統譬如機器人操作器的控制問題; 特別是, 單位的數量在中間層數增加[

    17 ] 並且因此學習進程有效地不被實施。方法[

    18 ] 由調整單位作用和連接重量, 或唯一單位作用最近提議, 而不是調整唯一的連接重量依照做在常規方法。它比常規遇見的煤斗被顯示[ 19], [20 ] 這methodgives 一個更加快速的學習進程和較少單位作用的數字在中間層數.

    Source(s): 自己
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