Genetic algorithm based neural networks for dynamical system modeling
Stephan Dreiseitl, Witold Aleksander Jacak · 2002
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.