Neuronal Architectures for Mixed Estimation and Stabilized Output Error Methods

Some architectures based m recurrent neural networks for parameter estimation using least squares mixed estimation and stabilized output error principles are presented. Some error bounds of these neuronal-circuit architectures relative to conventional ones are derived. The results should be useful for implementation of parameter estimation methods for real time applications