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A new structure adaptation algorithm for RBF networks and its application

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Abstract

An adaptation algorithm is developed for radial basis function network (RBFN) in this paper. The RBFN is adapted on-line for both model structure and parameters with measurement data. When the RBFN is used to model a non-linear dynamic system, the structure is adapted to model abrupt change of system operating region, while the weights are adapted to model the incipient time varying parameters. Two new algorithms are proposed for adding new centres while the redundant centres are pruned, which is particularly useful for model-based control. The developed algorithm is evaluated by modelling a numerical example and a chemical reactor rig. The performance is compared with a non-adaptive model.

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Acknowledgments

The project was funded by the U.K. EPSRC with the grant No. GR/N18697.

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Authors and Affiliations

  1. Control Systems Research Group, School of Engineering, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK

    D. L. Yu

  2. Department of Automation, Northeast University at Qinhuangdao, Qinhuangdao, China

    D. W. Yu

Authors
  1. D. L. Yu
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  2. D. W. Yu
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Correspondence to D. L. Yu.

Appendix

Appendix

See Table 1.

Table 1 Physical parameters of the chemical reactor rig
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Yu, D.L., Yu, D.W. A new structure adaptation algorithm for RBF networks and its application. Neural Comput & Applic 16, 91–100 (2007). https://doi.org/10.1007/s00521-006-0067-5

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  • DOI: https://doi.org/10.1007/s00521-006-0067-5

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