Open Science Research Excellence
%0 Journal Article
%A Ali Sarosh and  Dong Yun-Feng and  Muhammad Umer
%D 2014 
%J  International Journal of Aerospace and Mechanical Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 85, 2014
%T A TIPSO-SVM Expert System for Efficient Classification of TSTO Surrogates
%U http://waset.org/publications/9997678
%V 85
%X 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.

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