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Nawi, N.M., Khan, A., Rehman, M.Z., Aziz, M.A., Herawan, T. and Abawajy, J.H. (2014) An Accelerated Particle Swarm Optimization Based Levenberg Marquardt Back Propagation Algorithm. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A. and Huang, K., Eds., Neural Information Processing, International Conference on Neural Information Processing 2014, Vol. 8835, Springer, Cham, 245-253.
https://doi.org/10.1007/978-3-319-12640-1_30

has been cited by the following article:

• TITLE: Classification of Abandoned Areas for Solar Energy Projects Using Artificial Intelligence and Quantum Mechanics

  AUTHORS: David Gabriel de Barros Franco, Maria Teresinha Arns Steiner

  KEYWORDS: Solar Energy, Soil Reuse, Artificial Neural Networks, Quantum-Behaved Particle Swarm Optimization

  JOURNAL NAME: Journal of Geographic Information System, Vol.13 No.3, June 3, 2021

  ABSTRACT: The increasing demand for energy has intensified recently, requiring alternative sources to fossil fuels, which have become economically and environmentally unfeasible. On the other hand, the increasing land occupation in recent centuries is a growing problem, demanding greater efficiency, particularly in the reuse of abandoned areas, which has become an alternative. An interesting alternative would be installing energy facilities like solar, wind, biomass, and geothermal, in these areas. The objective of this paper is to develop a classification methodology, based on Artificial Intelligence (AI) and Quantum Theory (QT), to automatically carry out the classification of abandoned areas suitable for the settlement of these power plants. Artificial Neural Networks (ANNs) improved by the hybrid algorithm Quantum-behaved Particle Swarm Optimization (QPSO) together with the Levenberg-Marquardt Algorithm (LMA) were used for the classification task. In terms of Mean Squared Error (MSE), the QPSO-LMA approach achieved a decrease of 19.6% in relation to the classical LMA training with random initial weights. Moreover, the model’s accuracy showed an increase of 7.3% for the QPSO-LMA over the LMA. To validate this new approach, it was also tested on six different datasets available in the UCI Machine Learning Repository and seven classical techniques established in the literature. For the problem of installing photovoltaic plants in abandoned areas, the knowledge acquired with the solar dataset can be extrapolated to other regions.