A method for synthesizing neural-network models under incomplete data
Vitaly Schetinin, Victor S. Abrukov, А. И. Бражников · Optoelectronics Instrumentation and Data Processing · 2007
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.