Evaluation Model of Decoy Effectiveness Based on Improved GA-BP Neural Network

Abstract:

When evaluating decoy effectiveness by means of BP neural network, training sometimes failed because of local extremum problem. The genetic algorithm neural network model for evaluating camouflage effectiveness of decoy is created for this purpose. Two steps of evaluating by this method is necessary and a series of index is put forward. After initializing weights and executing genetic operation, we finally retrain the network to get the results which show that the method has fast convergence and the model reliable, effective and objective. This paper is meaningful to camouflage theory and application.

• Periodical: Advanced Materials Research (Volumes 108-111)
• Pages: 1205-1210
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.108-111.1205
• Online since: May 2010
• Authors: Chao He, Ling Li, Peng Liu
• Keywords: BP Neural Network (BPNN), Decoy, Effectiveness Evaluation, Genetic Algorithm (GA)

References

[1] Leung F H F, Lam H K, Ling S H，et a1. Tuning of the Structure and Parameters of a Neural Network Using an Improved Genetic Algorithm[J]. IEEE Transactions on Neural Networks, 2003, 14(1).

DOI: 10.1109/tnn.2002.804317

[2] Yao X. Evolving artificial neural networks[J]. Proc of the IEEE, 1999, 87(9): 1423-1447.

[3] D. Goldberg and R. Lingle, Alleles, loci and the traveling salesman problem, in Proc. of the first International Conference on Genetic Algorithm, Lawrence Erlbaum, Hillsdale, NJ, 1985, pp.154-159.

[4] M. Gen and R. Cheng, Genetic Algorithms and Engineering Design, Wiley, New York, (1997).

[5] W.M. Spears, Adapting crossover in evolutionary algorithms, in Proc. of 4th Annu. Conf. on Evolutionary Computing, Ed Fogel, MIT Press, Cambridge, MA, (1995).

[6] M. Srinivas and L.M. Patnaik, Adaptive probabilities of crossover and mutation in genetic algorithms, IEEE Trans. Systems, Man Cybernet. vol. 24, no. 4, pp.656-667, (1994).

DOI: 10.1109/21.286385

[7] Eshelman L , Schaffer J. Real-coded genetic algorithms and interval-schema[J]. Whitley D ed. Foundations of Genetic Algorithms 2. SanMateo: Morgan Kaufmann Publishers, 1992: 187-202.

DOI: 10.1016/b978-0-08-094832-4.50018-0

[8] Eric I Chang，Richard P Lippmann．Using genetic algorithms to improve pattern classification performance ． Proceedings of the 1990 Conference on advances in neural information processing systems 3[C]．Denver，Colorado，United States．797-803.

[9] LI Bing, XIE Jian-ying. Research on Self-adaptive Generation Gap Substitute Policy of Genetic Algorithm. Control Theory and Application, 2001, 18(1): pp.41-44.

[10] TANG Wan-mei. The Study of the Optimal Structure of BP Neural Network. System Engineering Theory and Practice, 2005, 25(10): pp.96-100.