Adaptive modelling with tunable RBF network using multi-innovation RLS algorithm assisted by swarm intelligence

Chen, H., Gong, Y. and Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 (2011) Adaptive modelling with tunable RBF network using multi-innovation RLS algorithm assisted by swarm intelligence. In: ICASSP'2011: the 36th International Conference on Acoustics, Speech and Signal Processing, 22-27 May 2011, Prague, Czech Republic, pp. 2132-2135.

Abstract/Summary

In this paper, we propose a new on-line learning algorithm for the non-linear system identification: the swarm intelligence aided multi-innovation recursive least squares (SI-MRLS) algorithm. The SI-MRLS algorithm applies the particle swarm optimization (PSO) to construct a flexible radial basis function (RBF) model so that both the model structure and output weights can be adapted. By replacing an insignificant RBF node with a new one based on the increment of error variance criterion at every iteration, the model remains at a limited size. The multi-innovation RLS algorithm is used to update the RBF output weights which are known to have better accuracy than the classic RLS. The proposed method can produces a parsimonious model with good performance. Simulation result are also shown to verify the SI-MRLS algorithm.