Improved Identification of Nonlinear MIMO Plants using New Hybrid FLANN-AIS Model

This identification of nonlinear MIMO plants finds extensive applications in stability analysis, controller design, modeling of intelligent instrumentation, analysis of power systems, modeling of multipath communication channels etc. For identification of such complex nonlinear plants, the recent trend of research is to employ nonlinear structures and to train their parameters by adaptive optimization algorithms. The area of artificial immune system (AIS) is emerging as an active and attractive field involving models, techniques and applications of greater diversity. In this paper a new optimization algorithm based on AIS is developed. This algorithm is hybridized with FLANN structure to develop a new model for efficient identification of nonlinear dynamic system. Simulation study of few benchmark MIMO identification problems is carried out to show superior performance of the proposed model over the standard GA and PSO based approach.