By Topic

Non-Parametric Model Structure Identification and Parametric Efficiency in Nonlinear State Dependent Parameter Models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Peter C. Young ; Director, Centre for Research on Environmental Systems and Statistics, Lancaster University, Lancaster LA1 5BL, U.K; Centre for Resource and Environmental Studies, Australian National University, Canberra.

Although neuro-fuzzy models provide a very useful general approach to the data-based modelling of nonlinear systems, their normal 'black box' nature is often a deterrent to their use in many of the natural sciences, where representation in terms of differential equations, or equivalent difference equations, is normally required and where the internal functioning and physical meaning of the model system is an important aspect of the modelling exercise. Moreover, identification of the model's internal structure can lead to considerable simplification of the model and the avoidance of over-parameterization, with important consequences as regards the statistical efficiency of the model parameter estimates. This paper introduces a non-parametric approach to model structure identification, based on recursive fixed interval smoothing, and shows how it can prove advantageous in the final parametric modelling of stochastic dynamic systems

Published in:

2006 International Symposium on Evolving Fuzzy Systems

Date of Conference:

7-9 Sept. 2006