-
- Koda Masato
- Institute of Policy and Planning Sciences, University of Tsukuba
-
- Okano Hiroyuki
- IBM Research, Tokyo Research Laboratory
書誌事項
- タイトル別名
-
- New Stochastic Learning Algorithm for Neural Networks
この論文をさがす
抄録
A new stochastic learning algorithm using Gaussian white noise sequence, referred to as Subconscious Noise Reaction (SNR), is proposed for a class of discrete-time neural networks with time-dependent connection weights. Unlike the back-propagation-through-time (BTT) algorithm, SNR does not require the synchronous transmission of information backward along connection weights, while it uses only ubiquitous noise and local signals, which are correlated against a single performance functional, to achieve simple sequential (chronologically ordered) updating of connection weights. The algorithm is derived and analyzed on the basis of a functional derivative formulation of the gradient descent method in conjunction with stochastic sensitivity analysis techniques using the variational approach.
収録刊行物
-
- 日本オペレーションズ・リサーチ学会論文誌
-
日本オペレーションズ・リサーチ学会論文誌 43 (4), 469-485, 2000
公益社団法人 日本オペレーションズ・リサーチ学会
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1390001204108639488
-
- NII論文ID
- 110001183929
-
- NII書誌ID
- AA00703935
-
- ISSN
- 21888299
- 04534514
-
- NDL書誌ID
- 5599947
-
- 本文言語コード
- en
-
- データソース種別
-
- JaLC
- NDL
- Crossref
- CiNii Articles
-
- 抄録ライセンスフラグ
- 使用不可