Structure selective updating for nonlinear models and radial basis function neural networks

✍ Scribed by W. Luo; S. A. Billings

• Publisher: John Wiley and Sons
• Year: 1998
• Tongue: English
• Weight: 242 KB
• Volume: 12
• Category: Article
• ISSN: 0890-6327
• DOI: 10.1002/(sici)1099-1115(199806)12:4<325::aid-acs492>3.0.co;2-q

✦ Synopsis

Selective model structure and parameter updating algorithms are introduced for both the online estimation of NARMAX models and training of radial basis function neural networks. Techniques for on-line model modification, which depend on the vector-shift properties of regression variables in linear models, cannot be applied when the model is non-linear. In the present paper new methods for on-line model modification are developed. These methods are based on selectively updating the non-linear model structure and therefore lead to a reduction in computational cost. A real data set is used to demonstrate the performance of the new algorithms.