Paper Title:
Sensor Compensation Based on Adaptive Ant Colony Neural Networks
  Abstract

In order to improve work stability and measurement accuracy of drilling inclinometer, and overcome the poor stability of Elman networks and lower compensation precision of genetic Elman neural networks, we combined ant colony algorithm and neural networks, using the Adaptive Ant Colony Algorithm that its pheromone evaporation factorand pheromone update strategy adjust adaptively to optimize Elman neural network weights and thresholds, and applied it to drilling inclinometer sensor compensation. Simulation results show that the compensation effect of adaptive ant colony Elman neural networks is better than that of Elman networks and genetic Elman networks, the compensation accuracy is 10-8.

  Info
Periodical
Advanced Materials Research (Volumes 301-303)
Chapter
Chapter 2: Measuring and Testing Techniques
Edited by
Riza Esa and Yanwen Wu
Pages
876-880
DOI
10.4028/www.scientific.net/AMR.301-303.876
Citation
X. F. Wang, C. C. Liang, J. G. Jiang, L. L. Ju, "Sensor Compensation Based on Adaptive Ant Colony Neural Networks", Advanced Materials Research, Vols. 301-303, pp. 876-880, 2011
Online since
July 2011
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Price
$32.00
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