A Method for Synthesizing Neural-Network Models under Incomplete Data

被引：0

作者：

Schetinin, V. G. ^{[1
]}

Abrukov, V. S. ^{[1
]}

Brazhnikov, A. I. ^{[1
]}

机构：

[1] Chuvash State Univ, Cheboksary, Russia

来源：

OPTOELECTRONICS INSTRUMENTATION AND DATA PROCESSING | 2007年 / 43卷 / 05期

关键词：

D O I：

10.3103/S8756699007050068

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Problems of synthesizing neural-network models under incomplete experimental data are described. The accuracy of the models is heavily dependent on their complexity. The proposed method allows self-organizing neural-network models of near-optimal complexity. Examples of synthesizing models of flame interferometry and growth of industrial production are presented.

引用

页码：433 / 440

页数：8

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