Abstract In this paper we extend the models discussed by Cohen (1992) by introducing an input term. This allows the resulting models to be utilized for system identification tasks. We prove that this model is stable in the sense that a bounded input leads to a bounded state when a minor restriction is imposed on the Lyapunov function. By employing this stability result, we are able to find a learning algorithm which guarantees convergence to a set of parameters for which the error between the model trajectories and the desired trajectories vanish.
University of New Mexico
Abdallah, Chaouki T.; James W. Howes; and Gregory L. Heileman. "A Learning Algorithm for Applying Cohen's Models to System Identification." (2012). http://digitalrepository.unm.edu/ece_fsp/160