Genetic algorithm based neural networks for dynamical system modeling

The modeling of nonlinear dynamical systems is one of the emergent application areas of artificial neural networks. In this paper we present a general methodology based on neural networks and genetic algorithms that can be applied to modeling of nonlinear dynamical systems. We describe a general methodology for modeling nonlinear systems with known rank (i.e., state space dimension) by feedforward networks with external delay units. We point out the shortcomings of this approach when the rank of the system is not known a priori. In this case, it is beneficial to employ genetic algorithms to search for neural networks that can model the nonlinear dynamical systems. Two genetic algorithms are presented for this case: one that determines the best feedforward network with external delay, and one that searches for a network with arbitrary topology and memory cells within each neuron.