Cognitive data science methods and models for engineering applications

Sangaiah, Arun Kumar; Pham, Hoang Trong Nghia; Chen, Mu-Yen; Lu, Huimin; Mercaldo, Francesco

doi:10.1007/s00500-019-04262-2

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…The modeling has many applications, such as prediction [1][2][3][4], pattern recognition [5][6][7][8], detection [9][10][11], and classification [12][13][14]. The backpropagation is a supervised algorithm was used in a radial basis mapping (RBM) network for the modeling; the value of the scale parameters was adjusted according to a cost [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm with Radial Basis Mapping Network for the Electricity Consumption Modeling

et al. 2020

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The modified backpropagation algorithm based on the backpropagation with momentum is used for the parameters updating of a radial basis mapping (RBM) network, where it requires of the best hyper-parameters for more precise modeling. Seeking of the best hyper-parameters in a model it is not an easy task. In this article, a genetic algorithm is used to seek of the best hyper-parameters in the modified backpropagation for the parameters updating of a RBM network, and this RBM network is used for more precise electricity consumption modeling in a city. The suggested approach is called genetic algorithm with a RBM network. Additionally, since the genetic algorithm with a RBM network starts from the modified backpropagation, we compare both approaches for the electricity consumption modeling in a city.

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