NDLI: Radar fault diagnosis based on adaptive genetic algorithm and neural network

Content Provider	IEEE Xplore Digital Library
Author	Pan Wei Xu Jiahe Liu Sili
Copyright Year	2011
Description	Author affiliation: Electric Detection Department, Shenyang Artillery Academy, Shenyang 110867, China (Pan Wei; Liu Sili) \|\| Department of Research Institute of Wood Industry Chinese Academy of Forestry, Beijing 100091, China (Xu Jiahe)
Abstract	A scheme for radar fault diagnosis is proposed. It is based on artificial neural networks whose learning sample comes from the Pspice simulation. For single circuit, single layer artificial neural networks are used. Aiming at the problems of slow rate of convergence and falling easily into part minimums in BP algorithm, a new improved genetic BP algorithm was put forward. To determine whether the network fall into part minimum point, a discriminator of part minimum was put forth in the training process of neural network. Genetic algorithm was used to revise the weights of the neural network if the BP algorithm fell into minimums. For large and complex system multiplayer artificial neural networks are used, as well as preprocessing layer and post processing layer. It can raise diagnosis precision, shorten to train time, raise fault tolerance and the development of fault diagnosis, effective carry out fault diagnosis. Circuit faults of certain radar are diagnosed using the simulation and neural network scheme put forward in this paper, which testified the validity of this diagnosis process.
Starting Page	3084
Ending Page	3088
File Size	181010
Page Count	5
File Format	PDF
ISBN	9781424487370
e-ISBN	9781424487387
DOI	10.1109/CCDC.2011.5968784
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Training adaptive genetic algorithm Artificial neural networks Genetics crossover probability mutation probability Circuit faults Integrated circuit modeling Genetic algorithms radar fault diagnosis
Content Type	Text
Resource Type	Article

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