http://iet.metastore.ingenta.com
1887

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

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

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IEE Proceedings - Control Theory and Applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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, MRAN's 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.

References

    1. 1)
      • M. AGARWAL . A systematic classification of neural-network-based control. IEEE Control Syst. Mag. , 2 , 75 - 93
    2. 2)
      • D. SADHUKHAN , S. FETEIH . F8 neurocontroller based on dynamic inversion. AIAA J. Guid., Control, Dynam. , 1 , 150 - 156
    3. 3)
      • K. NARENDRA , K. PARTHASARATHY . Identification and control of dynamic systems using neural networks. IEEE Trans., Neural Netw. , 1 , 4 - 26
    4. 4)
      • S. CHEN , S.A. BILLINGS , C. COUAN , P.M. GRANT . Practical identification of NARMAX models using radial basis function. Int. J. Control , 1327 - 1350
    5. 5)
      • S. CHEN , S.A. BILLINGS . Neural networks for system identification. Int. J. Control , 319 - 346
    6. 6)
      • C.L. CHEN , W.C. CHEN , F.Y. CHANG . Hybrid learning algorithm for Gaussian potential function networks. IEE Proc., Control Theory Appl. , 6 , 442 - 448
    7. 7)
      • J. MOODY , C.J. DARKEN . Fast learning in network of locally tuned processing units. Neural Computation , 281 - 294
    8. 8)
      • S. LEE , R.M. KIL . A Gaussian potential function network with hierarchically self-organizing learning. Neural Netw. , 207 - 224
    9. 9)
      • M.T. MUSAVI , W. AHMED , K.H. CHAN , K.B. FARIS , D.M. HUMMELS . On training of radial basis function classifiers. Neural Netw. , 595 - 603
    10. 10)
      • J. Platt . A resource allocating network for function interpolation. Neural Comput. , 213 - 225
    11. 11)
      • V. KADIRKAMANATHAN , M. NIRANJAN . A function estimation approach to sequential learning with neural networks. Neural Comput. , 954 - 975
    12. 12)
      • Y.W. LU , N. SUNDARARAJAN , P. SARATCHANDRAN . A sequential learning scheme for function approximation and using minimal radial basis neural networks. Neural Comput. , 2 , 1 - 18
    13. 13)
      • Y.W. LU , N. SUNDARARAJAN , P. SARATCHANDRAN . A sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Trans., Neural Netw. , 2 , 308 - 318
    14. 14)
      • Y.W. LU , N. SUNDARARAJAN , P. SARATCHANDRAN . Identification of time- varying non-linear systems using minimal radial basis function neural networks. IEE Proc., Control Theory Appl. , 1 , 1 - 7
    15. 15)
      • JUNGE, T.F., UNBEHAUEN, H.: `Off-line identification of nonlinear systems using structurally adaptive radial basis function networks', Proc. 35th Conf. on Decision and Control, December 1996, Kobe, Japan, p. 943–948.
    16. 16)
      • JUNGE, T.F., UNBEHAUEN, H.: `On-line identification of nonlinear time-variant systems using structurally adaptive radial basis function networks', Proc. Amer. Control Conf., 1997, Albuquerque, New Mexico, p. 1037–1041.
http://iet.metastore.ingenta.com/content/journals/10.1049/ip-cta_20000549
Loading

Related content

content/journals/10.1049/ip-cta_20000549
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address