Paper Title:
Control Simulation Study Based on Recurrent Generalized Congruence Neural Network
  Abstract

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)
Chapter
Chapter 22: Computer-Aided Engineering in Manufacturing
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
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Price
$32.00
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