Title :
Application of genetic algorithm and recurrent network to nonlinear system identification
Author_Institution :
Dept. of Guidance & Commun. Eng., Nat. Taiwan Ocean Univ., Keelung, Taiwan
Abstract :
Nonlinear system identification using recurrent neural network with genetic algorithm is presented. A continuous-time model of Hopfield neural network is used in this study. Its convergence properties are first evaluated. Then the model is implemented to identify nonlinear systems. Recurrent network´s operational factors of the system identification scheme are obtained by genetic algorithm. Mathematical formulations are introduced throughout the paper. After test, the proposed scheme can successfully identify nonlinear system within acceptable tolerance.
Keywords :
Hopfield neural nets; continuous time systems; convergence; genetic algorithms; identification; nonlinear control systems; recurrent neural nets; Hopfield neural network; continuous time systems; convergence properties; genetic algorithm; mathematical formulations; nonlinear system identification; operational factors; recurrent neural network; Artificial neural networks; Biological neural networks; Genetic algorithms; Multi-layer neural network; Neural networks; Neurofeedback; Neurons; Nonlinear systems; Recurrent neural networks; System identification;
Conference_Titel :
Control Applications, 2003. CCA 2003. Proceedings of 2003 IEEE Conference on
Print_ISBN :
0-7803-7729-X
DOI :
10.1109/CCA.2003.1223277
