@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}, }