APPLICATION OF PARTICLE SWARM OPTIMIZATION FOR PARAMETER ESTIMATION OF THE LOGISTIC MAP

This work considers the parameter estimation from measurements of the logistic map. The problem is solved in the context of optimization approach, which assumes minimization of a cost function that measures the difference between the time series obtained from the model equation and measurements. Complex dynamics of the logistic map leads to the multiextremal optimization problem. It requires using appropriate computational techniques. This work presents the application of particle swarm optimization in searching for the global minimum of the cost function.