Development of immunized pso algorithm and its application to hammerstein model identification

Combining the good features of particle swarm optimization (PSO) and artificial immune system (AIS) we propose a new Immunized PSO (IPSO) algorithm. This algorithm is used to identify generalized Hammerstein model by employing functional link artificial neural network (FLANN) architecture for the nonlinear static part and an adaptive linear combiners for the linear dynamic part of the model. Simulation study of few benchmark Hammerstein models is carried out through simulation study and the results obtained are compared with those obtained by standard PSO and AIS based method. Comparison of results demonstrate superior performance of the proposed methods over its PSO and AIS counterpart in terms of response matching, accuracy of identification and convergence speed achieved.