2022
DOI: 10.1016/j.pmcj.2022.101548
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Towards a better indoor positioning system: A location estimation process using artificial neural networks based on a semi-interpolated database

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Cited by 19 publications
(11 citation statements)
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“…In [86], a biharmonic spline interpolation method was proposed to expand the amount of wireless map data based on the original data collection. Combined with Feedforward Backpropagation (FFBP) Neural Network and General Regression Neural Network (GRNN), the position estimation of an artificial neural network based on a semiinterpolating database is designed.…”
Section: Real Data Acquisitionmentioning
confidence: 99%
“…In [86], a biharmonic spline interpolation method was proposed to expand the amount of wireless map data based on the original data collection. Combined with Feedforward Backpropagation (FFBP) Neural Network and General Regression Neural Network (GRNN), the position estimation of an artificial neural network based on a semiinterpolating database is designed.…”
Section: Real Data Acquisitionmentioning
confidence: 99%
“…The average value demonstrates how effective neuron network technologies are in solving this type of problem when compared to alternative approaches. Two different types of artificial neural networks (ANNs) are used in this study's Wi-Fi-fingerprinting localization system to estimate location [20]. These ANNs include generalized regression neural networks (GRNN) and feedforward backpropagation neural networks (FFBP).…”
Section: Related Workmentioning
confidence: 99%
“…The number of iterations is specified as 100. The PSO method uses the root-mean-square error (RMSE) [29] as a fitness function, as indicated in the following equation:…”
Section: Pso Algorithmmentioning
confidence: 99%
“…Other approaches being tested for measuring occupancy include environmental sensing [19], and data from social media platforms [20]. In addition, machine learning and statistical methods are employed to improve the accuracy and reliability of occupancy information [21][22][23]. The application of advanced computational techniques, such as artificial neural networks, the Markov chain model, decision trees, k-nearest neighbour, and support vector machines, in studies addressing occupancy of buildings is fast-growing [24].…”
Section: Introductionmentioning
confidence: 99%