Constrained LMS for Dynamic Flow Networks

Konstantinos Eftaxias, Clive Cheong Took, Bruno Venturini, David Arscott

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In this era of climate change, there is a growing need to offer adaptive learning algorithms in the optimisation of natural resources. These resources are typically optimised by evolutionary algorithms. However, evolutionary algorithms (EAs) are no longer adequate due to the `drift' component introduced by environmental factors such as flash flooding. We therefore propose a novel constrained Least Mean Squares (LMS) algorithm for the optimisation of flow networks. For rigor, we provide a stability analysis of our adaptive algorithm, which enables us to interpret the physical meaning of the network at equilibrium. We evaluate our proposed method against genetic algorithm (GA), the most common evolutionary algorithm. The results are promising: not only the proposed constrained LMS has a performance advantage over GA, but its computational cost is significantly lower making it more suitable for real-time applications.
Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks (IJCNN)
Number of pages5
ISBN (Electronic)978-1-5090-6182-2
ISBN (Print)978-1-5090-6183-9
Publication statusPublished - 3 Jul 2017

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