Research on Explosive Formulation Performance Parameter Prediction Method Based on GEP

Abstract:

Definition of homology gene, mutual exclusion gene, formulations gene and compatriots gene set has been given based on the characteristics of the energetic formulation components in this paper. The complex formula chromosome resolution rules have been designed to solve the energetic formulation component burst speed estimate problem combined with the GEP theories and test techniques. The test results showed that the performance prediction error of the detonation velocity is less than 3%.

• Periodical: Advanced Materials Research (Volumes 816-817)
• Pages: 180-184
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.816-817.180
• Online since: September 2013
• Authors: Dong Lei Wang, Li Juan Peng, Zhong Hua Lu
• Keywords: Detonation Velocity, Explosive, Formulation, GEP, Performance Prediction

References

[1] Dear R. G, Dynamic scheduling of Aircraft in the Near Terminal Area[R]. Technical Report R76-9, Flight Transportation Laboratory, M.I.T. September (1976).

[2] CiesielskiV, Scerri P. An anytime algorithm for scheduling aircraft landing times using genetic algorithms [J]. Australian journal of intelligent information processing system, 1997, (4): 206-213.

[3] Xu Xiaohao, YaoYuan. Application of genetic algorithm to aircraft sequencing in terminal area[J]Journal of Traffic and Transportation Engineering, 2004, 4(3): 121-126.

[4] Wang Laijun, Shi Zhongke. The Implement of Genetic Algorithms in Departure Sequencing Problem [J]. System Engineering-Theory& Practice. 2005, 9 (7): 119-125.

[5] Yang Qiuhui, You Zhisheng, Feng Ziliang, Hong Mei. Scheduling Arrival Aircrafts on Multiple Runways Based on an Improved Genetic Algorithm. [J]. JOURNAL OF SUCHUAN UNIVERSITY (ENGINEERING SCIENCE EDITION), 2006, 38 (2): 141-145.

[6] Yu Jiang, Luo Xiaoli. Applications of Genetic Algorithms on Aircraft Landing Schedule Problems [J]. Aeronautical Computing Technique. 2007, 37(3): 1-4.

[7] Bolender M A. Scheduling and Control Strategies for the Departure Problem in Air Traffic Control[D]. America University of Cincinnati, (2000).

[8] Srinivas L, Patnaik M. Adaptive probabilities of crossover and mutation in genetic algorithm [J]. IEEE Trans. On SMC, 1994, 24(4): 656-667.

DOI: 10.1109/21.286385

[9] C Ferreiral ·Gene Expression Programming in Problem Solving. [OL]·http: /www. Gene2expression2programming. com/gep/ webpapers/ Ferreira-WSC2001/Introduction. htm, (2001).

[10] FERREIRA C. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. [J]. Complex Systems, 2001, 13(2): 87-129.

[11] Tang Changjie, Peng Jing，Zhang Huan，Zhong Yixiao，Three new techniques for knowledge discover by gene expression programming—transgene, overlapped gene expression and backtracking evolution [J]Computer Application, 2005，25(9)：1978-(1981).