By Topic

Analysis of minimal radial basis function network algorithm for real-time identification of nonlinear dynamic 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
$33 $33
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

3 Author(s)
Y. Li ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore ; N. Sundararajan ; P. Saratchandran

A performance analysis is presented of the minimal resource allocating network (MRAN) algorithm for online identification of nonlinear dynamic systems. Using nonlinear time-invariant and time-varying identification benchmark problems, MRANs performance is compared with the online structural adaptive hybrid learning (ONSAHL) algorithm. Results indicate that the MRAN algorithm realises networks using fewer hidden neurons than the ONSAHL algorithm, with better approximation accuracy. Methods for improving the run-time performance of MRAN for real-time identification of nonlinear systems are developed. An extension to MRAN is presented, which utilises a winner neuron strategy and is referred to as the extended minimum resource allocating network (EMRAN). This modification reduces the computation load for MRAN and leads to considerable reduction in the identification time, with only a small increase in the approximation error. Using the same benchmark problems, results show that EMRAN is well suited for fast online identification of nonlinear plants

Published in:

IEE Proceedings - Control Theory and Applications  (Volume:147 ,  Issue: 4 )