Support vector regression based simultaneous identication of multiple NARMA plants

The simultaneous identification of multiple nonlinear plants using support vector regressions, and the methodology of multiple models, switching, and tuning, is proposed in this paper. Models are randomly initialised and, at each instant, they are assigned to a specific nonlinear plant, and adapted toward it. Accordingly, the models self-organise themselves in a manner so that, at any given instant of time, a plant is tracked by only one model. Two model assignment strategies are considered in this paper. The choice of the assignment strategy and the performance criteria play an important role in the model assignment, and hence the convergence.