Application of Neural Network Based on the Immune Genetic Algorithm in Failure Diagnosis

Li Li Zhao

doi:10.4028/www.scientific.net/AMR.706-708.650

Paper Titles

An Echo-Based Gait Phase Determination Method of Lower Limb Prosthesis
p.629

An Energy Aware Routing Based on Swarm Intelligence for Wireless Sensor Networks
p.635

Analyze the Transient Response of a Control Systems
p.639

Application and Research of Multi-Sensor Data Fusion for the Earlier Period Diagnosis in Induction Motors
p.644

Application of Neural Network Based on the Immune Genetic Algorithm in Failure Diagnosis
p.650

Application Research of ARM Embedded System in the Vision System of Chip Mounters
p.654

Automatic Data Acquisition System in Wheel-Rail Monitoring Test Bench
p.658

Based on Block Averaging Pre-Processing Differential Coherent Integration Weak Signal Acquisition Algorithm
p.663

Based on the Double SCM Real Time Control System Design of Drum Brake
p.667

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 706-708Application of Neural Network Based on the Immune...

Application of Neural Network Based on the Immune Genetic Algorithm in Failure Diagnosis

Abstract:

This paper designs the multilayer feed-forward neural network based on the immune genetic algorithm to solve the problem that BP algorithm is prone to get the local minimum in the failure diagnosis system. It is of both the learning ability and robustness of the neural network, as well as the strong global random searching ability of the immune genetic algorithm. The simulation results indicate the neural network can fulfill failure diagnosis of the complicated production better.

You might also be interested in these eBooks

Mechatronics and Intelligent Materials III

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 706-708)

Pages:

650-653

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.706-708.650

Citation:

Cite this paper

Online since:

June 2013

Authors:

Li Li Zhao

Keywords:

BP Network, Failure Diagnosis, Immune Genetic Algorithm

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J.Qiu S.M. Shahidehpour: A New Approach for Minimizing Power Loss and Improving Voltage Profile. IEEE Trans.Vol.PWRS-2,May,(1987)

Google Scholar

[2] N.I. Deeb S.M. Shahidehpour:An Efficient Technique for Reactive Power Dispatch Using a Revised Linear Programming Approach. Electric Power Sysem Research,1989(15),121~134

DOI: 10.1016/0378-7796(88)90016-8

Google Scholar

[3] Lyman C, Georgakis P R:Plant-wide control of the Tennessee Eastman problem [J]. Comput Chen Eng, 1993, 19: 322~331

Google Scholar

[4] Mcavoy T J, Ye N:Base control for the Tennessee Eastman probkem [J]. Comput Chem Eng, 1994, 18: 383~413

Google Scholar

[5] Ricker, N L:Decentralized control of the Tennessee Eastman challenge prceess [J]. Proc Control, 1996,6: 205~221

DOI: 10.1016/0959-1524(96)00031-5

Google Scholar

[6] Akbaryan F, Bishnoi P R:Fault diagnosis of multivariate sustems usng pattern recognition and multisensor data analysis technique [J].Computers and Chemical Engineering,2001,25: 1313~1339

DOI: 10.1016/s0098-1354(01)00701-3

Google Scholar