Application of Immune Algorithm on Input Variable Dimension Reduction in Neural Network Modeling

Abstract:

When the input variables are not mutual independence, it is easy to arise over fitting phenomenon using neural network modeling. So it leads to not only low accuracy of the proposed model but also the long time of modeling .In this paper using immune algorithm to optimize the input variables and selection, The simulation results show that the established model after optimization improves the performance, increases the accuracy and shortens the time of the modeling. Which shows that this algorithm can be effectively reduce the dimension of input variables of neural network.

• Periodical: Advanced Materials Research (Volumes 791-793)
• Pages: 1537-1540
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.791-793.1537
• Online since: September 2013
• Authors: Qing Ling Dai
• Keywords: Affinity Function, Expectation Reproduction Probability, Immune Algorithm, Independent Variable Dimension Reduction

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