Paper Title:
The Study of PNN Quality Control Method Based on Genetic Algorithm
  Abstract

For the probability neural network (PNN) algorithm is the non-surveillance's pattern taxonomic approach, the work load major problem, moreover the category number's selection will affect the cluster performance. How to optimize PNN enabled it to play a more effective role in the classified question, this paper proposed one use genetic algorithm optimization probability neural network method: introduction the auto-adapted mechanism genetic algorithm, to the probability neural network's parameter carries on the training, formed the supervised learning probability neural network based on the genetic algorithm, overcome the probability neural network existing algorithm flaw. Then introduces this model in the quality control, guaranteed that the production process is at the control state, achieves the quality control goal. Carries on the test through the simulation experiment to this algorithm, and with the probability neural network, the BP neural network carries on the comparative analysis, proved this method accuracy is high.

  Info
Periodical
Key Engineering Materials (Volumes 467-469)
Edited by
Dehuai Zeng
Pages
2103-2108
DOI
10.4028/www.scientific.net/KEM.467-469.2103
Citation
J. Li, S. L. Kan, P. Y. Liu, "The Study of PNN Quality Control Method Based on Genetic Algorithm", Key Engineering Materials, Vols. 467-469, pp. 2103-2108, 2011
Online since
February 2011
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Price
$32.00
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