This paper focuses on both the structure uncertainty and parameter uncertainty which have been widely explored in the literature of nonlinear system identification. An integrated analytic framework is proposed for automated neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance.
Published in:
Information Reuse and Integration (IRI), 2010 IEEE International Conference on
Date of Conference: 4-6 Aug. 2010