Patent ID: 8620631
Filing Date: 2013-12-31
Classification: G06F,G06N

Abstract:
1. A computerized method of identifying Hammerstein models with known nonlinearity structures using particle swarm optimization, comprising the steps of: (a) generating M input-output data points from a Hammerstein system to be identified, the Hammerstein system having both linear and nonlinear parts, wherein M is a pre-defined integer and the Hammerstein system is defined by  where k is an integer, the Hammerstein system having an input u(k), y(k) representing the output of the Hammerstein system, w(k) representing measurement noise of the Hammerstein system, and q representing a set of parameters describing the nonlinear part, with q (b) identifying the Hammerstein system by estimating the unknown parameters a (c) generating a set of random initial solutions for zeros and poles of the linear part and also for the sets of unknown parameters a (d) evaluating a fitness function F for the set of random initial solutions, wherein the fitness function F is given by  L being an integer defined by representing the non-measured intermediate variable x(k) as x(k)=c (e) minimizing the fitness function F to generate the estimated unknown parameters â (f) applying particle swarm optimization to the set of solutions by determining the most fit zeros and poles of the linear part calculated in step (d); (g) generating a new set of solutions composed of the most fit zeros and poles of the linear part and with the estimated parameters â (h) repeating steps (d) through (g) for a predetermined number of generations.