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Open AccessJournal ArticleDOI

Pollution level predictor using artificial neural networks trained with galactic swarm optimization algorithms

Nilay Nigam, +1 more
- Vol. 263, Iss: 4, pp 042093
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The article was published on 2017-11-01 and is currently open access. It has received 1 citations till now. The article focuses on the topics: Swarm behaviour & Artificial neural network.

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Optimal total harmonic distortion minimization in multilevel inverter using improved whale optimization algorithm

TL;DR: The proposed IWOA provides better computational efficiency, improved consistency and faster convergence compared to the older whale optimization algorithm (WOA) with minimal tuning of the algorithm’s parameters.
References
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Journal ArticleDOI

Air quality model performance evaluation

TL;DR: A review is given of a set of model evaluation methodologies, including the BOOT and the ASTM evaluation software, Taylor’s nomogram, the figure of merit in space, and the CDF approach.
Journal ArticleDOI

A neural network forecast for daily average PM10 concentrations in Belgium

TL;DR: In this article, the authors used a neural network to forecast the daily average PM 10 concentrations in Belgium one day ahead based upon measurements from ten monitoring sites during the pe riod 1997 -2001 and upon ECMWF simulations of meteorological parameters.
Journal ArticleDOI

A model inter-comparison study focussing on episodes with elevated PM10 concentrations

TL;DR: In this paper, five three-dimensional chemical transport models of different complexity were applied to Central Europe to assess the ability of models to reproduce PM10 concentrations under highly polluted conditions, and the results showed that there is an increasing underestimation of primary and secondary species with increasing observed PM10.
Journal ArticleDOI

Galactic Swarm Optimization

TL;DR: Extensive simulation results show that the GSO algorithm proposed in this paper converges faster to a significantly more accurate solution on a wide variety of high dimensional and multimodal benchmark optimization problems.
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