A method for indoor Wi-Fi location based on improved back propagation neural network

Chen, Jinghui; Dong, Chen; He, Guotian; Zhang, Xiaoyu

doi:10.3906/elk-1812-13

Cited by 14 publications

(8 citation statements)

References 13 publications

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“…Other metrics used are the mean squared error (MSE) and the median error (MedianError). On the other hand, 14 papers report the error of success in a certain precision based in percentage [16,23,28,29,36,42,47,48,57,52,53,67,54,60]. Unfortunately, three articles do not show results clearly or are based on items different from those analyzed in this review [79,45,50].…”

Section: Metricsmentioning

confidence: 93%

New trends in indoor positioning based on WiFi and machine learning: A systematic review

Bellavista-Parent,

Torres-Sospedra,

Perez-Navarro

2021

Preprint

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Currently there is no standard indoor positioning system, similar to outdoor GPS. However, WiFi signals have been used in a large number of proposals to achieve the above positioning, many of which use machine learning to do so. But what are the most commonly used techniques in machine learning? What accuracy do they achieve? Where have they been tested? This article presents a systematic review of works between 2019 and 2021 that use WiFi as the signal for positioning and machine learning models to estimate indoor position. 64 papers have been identified as relevant, which have been systematically analyzed for a better understanding of the current situation in different aspects. The results show that indoor positioning based on WiFi trends use neural network-based models, evaluated in empirical experiments. Despite this, many works still conduct an assessment in small areas, which can influence the goodness of the results presented.

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Section: Metricsmentioning

confidence: 93%

New trends in indoor positioning based on WiFi and machine learning: A systematic review

Bellavista-Parent,

Torres-Sospedra,

Perez-Navarro

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Other metrics used are the Mean Squared Error (MSE) and the Median Error. Another important metric is the percentage, which is used in one way or another in 36 papers [22,24,25,[55][56][57][58][59][60][61][62][63][64][65]. Unfortunately, two articles do not show their results clearly; papers [66,67] only show a graph, however, it is difficult to determine the obtained results from the image.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review

Bellavista-Parent

Torres-Sospedra

Pérez-Navarro

2022

Sensors

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Nowadays, there are a multitude of solutions for indoor positioning, as opposed to standards for outdoor positioning such as GPS. Among the different existing studies on indoor positioning, the use of Wi-Fi signals together with Machine Learning algorithms is one of the most important, as it takes advantage of the current deployment of Wi-Fi networks and the increase in the computing power of computers. Thanks to this, the number of articles published in recent years has been increasing. This fact makes a review necessary in order to understand the current state of this field and to classify different parameters that are very useful for future studies. What are the most widely used machine learning techniques? In what situations have they been tested? How accurate are they? Have datasets been properly used? What type of Wi-Fi signals have been used? These and other questions are answered in this analysis, in which 119 papers are analyzed in depth following PRISMA guidelines.

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“…The BP neural network has the ability of self-organization, self-learning and self-adaptation, and its principle is simple and easy to implement [ 20 ]. It has been widely used in many fields.…”

Section: Theory Related To Bp Neural Networkmentioning

confidence: 99%

Membrane Fouling Prediction Based on Tent-SSA-BP

et al. 2022

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In view of the difficulty in obtaining the membrane bioreactor (MBR) membrane flux in real time, considering the disadvantage of the back propagation (BP) network in predicting MBR membrane flux, such as the local minimum value and poor generalization ability of the model, this article introduces tent chaotic mapping in the standard sparrow search algorithm (SSA), which improves the uniformity of population distribution and the searching ability of the algorithm (used to optimize the key parameters of the BP network). The tent sparrow search algorithm back propagation network (Tent-SSA-BP) membrane fouling prediction model is established to achieve accurate prediction of membrane flux; compared to the BP, genetic algorithm back propagation network (GA-BP), particle swarm optimization back propagation network (PSO-BP), sparrow search algorithm extreme learning machine(SSA-ELM), sparrow search algorithm back propagation network (SSA-BP), and Tent particle swarm optimization back propagation network (Tent–PSO-BP) models, it has unique advantages. Compared with the BP model before improvement, the improved soft sensing model reduces MAPE by 96.76%, RMSE by 99.78% and MAE by 95.61%. The prediction accuracy of the algorithm proposed in this article reaches 97.4%, which is much higher than the 48.52% of BP. It is also higher than other prediction models, and the prediction accuracy has been greatly improved, which has some engineering reference value.

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A method for indoor Wi-Fi location based on improved back propagation neural network

Cited by 14 publications

References 13 publications

New trends in indoor positioning based on WiFi and machine learning: A systematic review

New trends in indoor positioning based on WiFi and machine learning: A systematic review

Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review

Membrane Fouling Prediction Based on Tent-SSA-BP

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