Identification of nonlinear systems with evolving networks

This paper discusses and shows how computation intensive nonlinear identification problems can be computed efficiently using evolving networks and clusters of workstations. Simulations are conducted to study the performance of this approach with different nonlinear systems. Results of developing parallel algorithms for system identification are discussed.