AMOS-a new hybrid evolutionary algorithm for continuous time systems

Author

Santosuosso, Giovanni L.

Author_Institution

Dipt. di Ingegneria Elettronica, Rome Univ., Italy

Volume

5

fYear

2001

fDate

2001

Firstpage

4920

Abstract

A novel evolutionary algorithm called atomic metaphor optimization strategy (AMOS) is proposed, which is designed for real-time analog optimization problems. This new evolutionary algorithm is integrated with the continuous time adaptive observer algorithm based on the Lyapunov stability theory, developed for classes of approximating functions with linear parametrization. The combined hybrid algorithm is applied to the online modeling of continuous-time nonlinear systems, via a nonlinearly parametrized neural approximation of the system dynamics

Keywords

Lyapunov methods; continuous time systems; evolutionary computation; nonlinear systems; observers; simulated annealing; AMOS; Lyapunov stability theory; atomic metaphor optimization strategy; continuous time adaptive observer algorithm; continuous time systems; hybrid evolutionary algorithm; nonlinear systems; nonlinearly parametrized neural approximation; real-time analog optimization problems; simulated annealing; Approximation algorithms; Continuous time systems; Data structures; Evolutionary computation; Genetic algorithms; Linear approximation; Neural networks; Real time systems; Simulated annealing; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2001. Proceedings of the 40th IEEE Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-7061-9

Type

conf

DOI

10.1109/.2001.980988

Filename

980988