期刊
CONNECTION SCIENCE
卷 26, 期 3, 页码 195-214出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/09540091.2014.907555
关键词
solid fuel ignition model; particle swarm optimisation; sequential quadratic programming; boundary value problems
In this paper, an efficient procedure based on the neural networks methodology is presented for the solution of the fuel ignition model in one dimension. The neural networks were optimised with the particle swarm optimisation algorithm hybridised with sequential quadratic programming. The accuracy and convergence of the scheme are analysed by Monte Carlo simulations and their statistical analyses for three test cases of the problem represented by Bratu-type equations. It was found that the hybrid approach converges in all cases, and can solve the problem with higher accuracy and reliability than most of the methodologies used so far to solve this problem.
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