@article{(Open Science Index):https://publications.waset.org/pdf/9997678,
	  title     = {A TIPSO-SVM Expert System for Efficient Classification of TSTO Surrogates},
	  author    = {Ali Sarosh and  Dong Yun-Feng and  Muhammad Umer},
	  country	= {},
	  institution	= {},
	  abstract     = {Fully reusable spaceplanes do not exist as yet. This implies that design-qualification for optimized highly-integrated forebody-inlet configuration of booster-stage vehicle cannot be based on archival data of other spaceplanes. Therefore, this paper proposes a novel TIPSO-SVM expert system methodology. A non-trivial problem related to optimization and classification of hypersonic forebody-inlet configuration in conjunction with mass-model of the two-stage-to-orbit (TSTO) vehicle is solved. The hybrid-heuristic machine learning methodology is based on two-step improved particle swarm optimizer (TIPSO) algorithm and two-step support vector machine (SVM) data classification method. The efficacy of method is tested by first evolving an optimal configuration for hypersonic compression system using TIPSO algorithm; thereafter, classifying the results using two-step SVM method. In the first step extensive but non-classified mass-model training data for multiple optimized configurations is segregated and pre-classified for learning of SVM algorithm. In second step the TIPSO optimized mass-model data is classified using the SVM classification. Results showed remarkable improvement in configuration and mass-model along with sizing parameters.
},
	    journal   = {International Journal of Aerospace and Mechanical Engineering},
	  volume    = {8},
	  number    = {1},
	  year      = {2014},
	  pages     = {209 - 217},
	  ee        = {https://publications.waset.org/pdf/9997678},
	  url   	= {https://publications.waset.org/vol/85},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 85, 2014},
	}