Title :
Process modeling method based on an improved Elman Neural Network
Author :
Qi, Zhongji ; Liu, Mandan ; Wang, Honggang
Author_Institution :
Coll. of Inf. Sci. & Technol., East China Univ. of Sci. & Technol., Shanghai
Abstract :
A new improved Elman neural network, OAIF-Elman(output-add-input feedback Elman) network is presented based on the Elman neural network in this paper. The new Elman network is endowed with state-feedback and output-feedback by an additional output-context layer, which is added to the former input to get a new input of network. It is applied for setting up ethylene cracking severity soft sensor model, and the parameters of network are optimized by genetic algorithm. The simulation results indicate that the OAIF-Elman network presented in this paper have better fitting and predictive result than basic Elman and other 4 kinds of improved Elman network under the same conditions, meanwhile the satisfying model of cracking severity is obtained.
Keywords :
genetic algorithms; neural nets; state feedback; Elman neural network; ethylene cracking severity; output-add-input feedback Elman network; output-context layer; process modeling method; soft sensor model; state feedback; Automation; Chemical technology; Educational institutions; Genetic algorithms; Intelligent control; Neural networks; Neurofeedback; Output feedback; Predictive models; Recurrent neural networks; Cracking Severity; Elman Network; Genetic Algorithm; Soft-sensing;
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
DOI :
10.1109/WCICA.2008.4594210