Title :
Optimal identification method of nonlinear system based on GA-GHNNs P
Author :
Xiao-Fei, Lin ; Mu-Yun, Weng
Author_Institution :
Telecommun. Eng. Inst., Air Force Univ. of Eng., Xi´´an, China
Abstract :
Gaussian-Hopfield neural network algorithm (GHNNs) is the most commonly used method of solving the identification problem of nonlinear systems, but learning rule (LMS rule) is easy to fall into local optimum. Genetic algorithm (GA) has globally optimal ability and can solve the locally optimal problem well. This paper puts forward GA-GHNNs algorithm and uses GA algorithm to solve the optimum parameters of GHNNs network. And finally, it carries out simulation experiments to prove the validity of the algorithm. Simulation results also show that this method has the ability to distinguish nonlinear systems.
Keywords :
Gaussian processes; Hopfield neural nets; adaptive control; genetic algorithms; learning systems; least mean squares methods; neurocontrollers; nonlinear control systems; Gaussian-Hopfield neural network algorithm; LMS rule; genetic algorithm; learning rule; nonlinear system; optimal identification method; Delay effects; Electronic mail; Gaussian processes; Genetic algorithms; Least squares approximation; Neural networks; Nonlinear dynamical systems; Nonlinear systems; System identification;
Conference_Titel :
Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-4519-6
Electronic_ISBN :
978-1-4244-4520-2
DOI :
10.1109/ICCSIT.2009.5234495