Performance Improvement and Optimization in Networks Using Ensemble Kalman Filters

Abstract

Ensemble Kalman filters (EnKFs) is a statistics assimilation technique extensively used for the most influential kingdom estimation and forecasting. This chapter investigates the potential of ensemble Kalman filters for area networks and their use within the international optimization of network overall performance. It highlights the advantages of using ensemble Kalman filters in phrases of higher overall performance, quicker convergence, and progressed robustness with admiration to local optimization schemes. Moreover, the chapter gives an easy yet effective option to the problem of model error and uncertainty that is usually located when handling massive-scale networks. Subsequently, it validates the proposed method by comparing its outcomes with the ones acquired with local optimization methods. The results of this comparison show that ensemble Kalman filters outperform neighborhood optimization schemes in terms of network overall performance, scalability, and robustness.