By Topic

A Pattern Recognition Approach to the Classification of Nonlinear Systems

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
$31 $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

2 Author(s)
Saridis, G.N. ; School of Electrical Engineering, Purdue University, Lafayette, Ind. 47907. ; Hofstadter, Robert F.

A fundamental problem in system modeling and theory is the characterization of the structure of an unknown nonlinear stochastic system when only input-output measurements are available. A method of classifying nonlinear stochastic systems, using pattern recognition and a pattern vector constructed from the input-output data, is proposed for ten stated classes of low-order nonlinear systems. The method is capable of extension to additional classes of nonlinear systems. Extensive experimental results are given to show that classification of an unknown nonlinear system, with respect to basic structural properties, can be and accomplished with a very high probability of correct classification. Various applications of the classification procedure are given, particularly in the areas of systems modeling, self-organizing control systems, and learning control systems.

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:SMC-4 ,  Issue: 4 )