Parameter estimation of MIMO bilinear systems using a Levy shuffled frog leaping algorithm

Parameter identification of bilinear systems has been considered as an evolutionary computing algorithm-based optimization problem in this paper. A new Levy shuffled frog leaping algorithm (LSFLA), which is an improved version of the conventional shuffled frog leaping algorithm (SFLA), has been designed and has been applied for this parameter identification task. LSFLA offers enhanced local search behaviour in comparison with other traditional evolutionary computing algorithms. The ability of the new algorithm in accurately modeling parameters in single input single output (SISO) as well as multiple input multiple output (MIMO) has been checked using an extensive simulation study. The parameter estimation efficiency of the new scheme has been compared with that obtained using other popular evolutionary computing algorithms and the simulation study reveals the enhanced parameter identification ability of the proposed LSFLA.