Parameter estimation using biologically inspired methods

Identification of nonlinear systems has drawn much attention in recent years. This paper presents a new identification algorithm for Hammerstein models based on bacterial foraging. In specific, the biomimicry of the bacterial chemotaxis algorithm is used to identify model parameters. A flowchart of this identification algorithm is given. Simulation and Comparison studies show that the proposed bacterial foraging based approach outperforms the particle swarm optimization in terms of both convergence and precision.