Training Back-Propagation Neural Network for Target Localization Using Improved Particle Swarm Optimization

• Published in: Applied Mechanics and Materials Vol. 333-335; pp. 1384 - 1387
• Main Authors: Ju, Xiang, Pan, Jin Xiao, Wang, Li Ming, Han, Yan, Yao, Jin Jie
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 01.07.2013
• Subjects: Target Localization; Back-Propagation Neural Network; Particle Swarm Optimization (PSO); Time Difference of Arrival (TDOA)
• ISBN: 3037857501; 9783037857502
• ISSN: 1660-9336; 1662-7482; 1662-7482
• DOI: 10.4028/www.scientific.net/AMM.333-335.1384

Target localization technology has been intensively studied and broadly applied in many fields. This paper presents one improved particle swarm optimization technique in training a back-propagation neural network for position estimation in target localization. The proposed scheme combines particle swarm optimization (PSO), back-propagation neural network (BP), adaptive inertia weight and hybrid mutation, called IPSO-BP. To verify the proposed IPSO-BP approach, comparisons between the PSO-based BP approach (PSO-BP) and general back-propagation neural network (BP) are made. The computational results show that the proposed IPSO-BP approach exhibits much better performance in the training process and better prediction ability in the validation process than those using the other two base line approaches.