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Comparison of Various Neural Network Algorithms Used for Location Estimation in Wireless Communication

Shashank Mishra, G.S. Tripathi

In recent years, Artificial Neural Network has been the topic of great interest in the field of Wireless Communication in different ways. To enhance the accuracy of location estimation, we propose the various neural network algorithms i.e. Resilient back-propagation (Rprop), Levenburg-Marquardt (LM), Conjugate gradient with Polak-Ribiere Updates (CGP), Conjugate gradient with Fletcher-Reeves Updates (CGF) utilizing the time of arrival (TOA) measurement information in presence of NLOS error to locate the mobile station (MS) with three base station available. Computer simulations have been performed by using Neural Network Toolbox for MATLAB and then performance of various algorithms has been compared in terms of root mean square (RMS) error.