Control Simulation Study Based on Recurrent Generalized Congruence Neural Network

Abstract:

Article Preview

In order to meet the real-time demand of neural network control system, the structure and algorithm of self-tuning PID control system based on recurrent generalized congruence neural network(RGCNN) with fast convergence are presented, in which the improved recurrent generalized congruence neural network is adopted for identifier, and the single generalized congruence neuron with three inputs is used as controller. The simulation results of nonlinear dynamical control system show that the proposed RGCNN control system responses quickly and is stable, i.e., the proposed control system based on RGCNN is effective and feasible.

Info:

Periodical:

Advanced Materials Research (Volumes 383-390)

Edited by:

Wu Fan

Pages:

5691-5696

DOI:

10.4028/www.scientific.net/AMR.383-390.5691

Citation:

T. Y. Yan "Control Simulation Study Based on Recurrent Generalized Congruence Neural Network", Advanced Materials Research, Vols. 383-390, pp. 5691-5696, 2012

Online since:

November 2011

Authors:

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.