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
收藏
页码：433 / 440
页数：8
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