DocumentCode
1842529
Title
New methods to train a BP network and their application
Author
Chen, Jun Qing ; Jiang, Jing Ping
Author_Institution
Dept. of Electr. Eng., Zhejiang Univ., Hangzhou, China
Volume
3
fYear
1999
fDate
1999
Firstpage
1702
Abstract
To estimate reaction consistency of the continuous stirred tank reactor (CSTR) system a kind of hybrid genetic algorithms (HGA) is presented. It combines the merits of BP algorithm and canonical genetic algorithms (CGA). The BP algorithm is inserted between the two reproduction parts-selection and reproduction, the results demonstrate the proposed HGAs can get quite good effect. We also replace CGA with an evolution strategy, which the simulation results show gives more accurate results
Keywords
backpropagation; chemical technology; genetic algorithms; neural nets; state estimation; BP network training; CGA; CSTR; HGA; backpropagation; canonical genetic algorithms; continuous stirred tank reactor; evolution strategy; hybrid genetic algorithms; neural net; reaction consistency estimation; Biological cells; Continuous-stirred tank reactor; Decoding; Encoding; Genetic algorithms; Genetic mutations; Instruments; Neural networks; Nonlinear systems; State estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-5529-6
Type
conf
DOI
10.1109/IJCNN.1999.832631
Filename
832631
Link To Document