Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization

The research of localization technology based on received signal strength and machine learning has recently attracted a lot of attentions, since with the help of enough labeled training data this technology is able to achieve high positioning accuracy. However, it is an expensive job to collect enough labeled training data in the broad outdoor space. In order to reduce the cost of building and maintaining training database, semi-supervised extreme learning machine is applied to solve the cellular network localization in this article. However, the performance of this algorithm is sensitive to the values of the hyper parameters. Without any systematic guidance, the optimal hyper parameters can only be selected by experienced workers through trial and error. To address this problem, we propose a novel algorithm by combining particle swarm optimization and semi-supervised extreme learning machine to automatically select the optimal hyper parameters of semi-supervised extreme learning machine in this article. The experiments demonstrate that applying particle swarm optimization in our optimization framework makes the hyper parameters of semi-supervised extreme learning machine algorithm self-adaptive in different conditions. Moreover, the proposed method is more stable than the general semi-supervised extreme learning machine and outperforms other compared methods.