Application of RBF network based on Artificial Immune Algorithm to predict gas pipeline load

Author

Hong, Liu ; Qingheng, Zeng ; Min, Yang ; Mujiao, Fan

Author_Institution

Chongqing Univ. of Sci. & Technol., Chongqing, China

fYear

2010

fDate

7-9 July 2010

Firstpage

962

Lastpage

967

Abstract

Aiming to the features of the load variation of gas pipeline, it is suggested Fuzzy Logic system and RBF Nerve Network Based on Artificial Immune Algorithm is used to predict the load of gas pipeline. The fuzzy logic system is applied to predict the load error and the error variation rate. Then, the RBF Nerve Network Based on Artificial Immune Algorithm is used to predict the load of gas pipeline. The results showed that the relative errors are all less than 8%, which proved that the novel RBF neural network model based on Artificial Immune Algorithm has less calculation, high precision and good generalization ability.

Keywords

artificial immune systems; fuzzy logic; fuzzy systems; pipelines; radial basis function networks; RBF nerve network; RBF neural network; artificial immune algorithm; fuzzy logic system; gas pipeline load prediction; load variation; Artificial neural networks; Data models; Immune system; Load modeling; Pipelines; Prediction algorithms; Radial basis function networks; Artificial Immune Algorithm; Gas Pipeline network; Load; Prediction; RBF Nerve network;

fLanguage

English

Publisher

ieee

Conference_Titel

Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-8041-8

Type

conf

DOI

10.1109/COGINF.2010.5599769

Filename

5599769