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

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volumes 333-335)
• Pages: 1384-1387
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.333-335.1384
• Online since: July 2013
• Authors: Jin Jie Yao, Xiang Ju, Li Ming Wang, Jin Xiao Pan, Yan Han
• Keywords: Back-Propagation Neural Network, Particle Swarm Optimization (PSO), Target Localization, Time Difference of Arrival (TDOA)

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