NDLI: An Improved Probabilistic Neural Network with GA Optimization

Content Provider	IEEE Xplore Digital Library
Author	Huafen Yang You Yang
Copyright Year	2012
Abstract	Probabilistic Neural Network (PNN) was applied to prediction mainly. Over the traditional neural network, less time was cost by PNN in network architecture determining and training. But the smoothing parameter used in the estimation results was a user-defined constant. How to determine this parameter's value is a crucial problem in PNN. Combined with adaptive genetic algorithm (GA), a novel PNN was proposed. Crossover probability c p and mutation probability m p were employed to optimize the smoothing parameter. These two probability factors were adaptive, they would vary with the fitness of population. A reasonable search breadth and depth was finished by the GA with c p and m p. The prediction accuracy of PNN was increased because of the smoothing parameters optimization. The simulation experiments demonstrated that the improved PNN model was effective. The prediction precision was increased more.
Starting Page	76
Ending Page	79
File Size	452866
Page Count	4
File Format	PDF
ISBN	9781467304702
DOI	10.1109/ICICTA.2012.26
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Smoothing methods Fault diagonisis Smoothing parameters Neurons Probabilistic logic Optimization Biological neural networks Probablistic nerual network Genetic algorithms
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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