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

— 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 (SIMRLS) 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.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Hao Chen, Yu Gong, Xia Hong
• Keywords: Algorithm, ICASSP 2011, Model, Output Weights, Signal Processing