Application of Particle Swarm Optimization Algorithm to Neural Network Training Process in the Localization of the Mobile Terminal

Karwowski, Jan; Okulewicz, Michał; Legierski, Jarosław

doi:10.1007/978-3-642-41013-0_13

Cited by 15 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The presented solution is based on preliminary work [13]. Other analyses for the building can be found in [28,29,30]. However, these works are focused on the localisation algorithms and their results cannot be directly compared with our results.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Missing Access Points in Indoor Positioning System †

Górak

Luckner

2018

Sensors

View full text Add to dashboard Cite

The paper presents a Wi-Fi-based indoor localisation system. It consists of two main parts, the localisation model and an Access Points (APs) detection module. The system uses a received signal strength (RSS) gathered by multiple mobile terminals to detect which AP should be included in the localisation model and whether the model needs to be updated (rebuilt). The rebuilding of the localisation model prevents the localisation system from a significant loss of accuracy. The proposed automatic detection of missing APs has a universal character and it can be applied to any Wi-Fi localisation model which was created using the fingerprinting method. The paper considers the localisation model based on the Random Forest algorithm. The system was tested on data collected inside a multi-floor academic building. The proposed implementation reduced the mean horizontal error by 5.5 m and the classification error for the floor’s prediction by 0.26 in case of a serious malfunction of a Wi-Fi infrastructure. Several simulations were performed, taking into account different occupancy scenarios as well as different numbers of missing APs. The simulations proved that the system correctly detects missing and present APs in the Wi-Fi infrastructure.

show abstract

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Missing Access Points in Indoor Positioning System †

Górak

Luckner

2018

Sensors

View full text Add to dashboard Cite

show abstract

“…2) Repeat the following steps until the gbest solution does not change anymore or the maximum number of iterations is reached. a. Update the number of hidden layers, the number of neurons in each layer, the velocity of the number of hidden layers and the number of neurons in each particle according to the Equations (4) through (7). b.…”

Section: Pso-based Parameter Optimization Modelmentioning

confidence: 99%

“…Several researchers have explored parameter optimization of various machine learning algorithms. PSO has been applied to train shallow neural networks [7]. There are a number of studies about specifying and optimizing the initial weights of Artificial Neural Networks (ANN) learning [8] [9] [10] [11].…”

Section: Introductionmentioning

confidence: 99%

Parameters optimization of deep learning models using Particle swarm optimization

Qolomany

Maabreh

Al-Fuqaha

et al. 2017

2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)

View full text Add to dashboard Cite

Deep learning has been successfully applied in several fields such as machine translation, manufacturing, and pattern recognition. However, successful application of deep learning depends upon appropriately setting its parameters to achieve high-quality results. The number of hidden layers and the number of neurons in each layer of a deep machine learning network are two key parameters, which have main influence on the performance of the algorithm. Manual parameter setting and grid search approaches somewhat ease the users' tasks in setting these important parameters. Nonetheless, these two techniques can be very time-consuming. In this paper, we show that the Particle swarm optimization (PSO) technique holds great potential to optimize parameter settings and thus saves valuable computational resources during the tuning process of deep learning models. Specifically, we use a dataset collected from a Wi-Fi campus network to train deep learning models to predict the number of occupants and their locations. Our preliminary experimentsindicate that PSO provides an efficient approach for tuning the optimal number of hidden layers and the number of neurons in each layer of the deep learning algorithm when compared to the grid search method. Our experiments illustrate that the exploration process of the landscape of configurations to find the optimal parameters is decreased by 77 % -85%. In fact, the PSO yields even better accuracy results.

show abstract

“…PSO is most popular among other evolutionary algorithms because of ease of implementation and requirement of tuning of few parameters. PSO has recently been employed in the field of an optimization problem such as training of neural network [41].…”

Section: Training Of Multi-layer Perceptronsmentioning

confidence: 99%

Training of Multilayer Perceptrons with Improved Particle Swarm Optimization for the Heart Diseases Prediction

Jp¹,

Ss²

2017

Int J Swarm Intel Evol Comput

View full text Add to dashboard Cite

IntroductionThe graphical depiction of the heart beats in the form of electrical signals is known as electrocardiogram (ECG). The abnormal rhythms of the heart beats are termed as cardiac arrhythmias. Some arrhythmias are life threatening. Therefore there is need to identify the heart conditions of cardiac patients. The identification of cardiac arrhythmias in early stage can save the patients from sudden cardiac arrest.A variation in the consecutive cardiac beats is referred to as heart rate variability (HRV). By means of HRV analysis technique cardiac health can usually be computed. An estimation of HRV is recently being adopted as investigation tool for recognition of heart abnormalities in cardiology. AbstractThe study of Heart rate variability is recently gained momentum for an estimation of heart health. This paper suggests a new approach for enhancement of the prediction accuracy of Multi-Layer Perceptrons (MLP) neural network using improved Particle Swarm Optimization (IPSO) technique. The IPSO computes the weights and biases of MLP for the more accurate prediction of the cardiac arrhythmia classes. This study for heart condition prediction involves selection of Three types of heart signals including Left Bundle Branch Block (LBBB), Normal Sinus Rhythm (NSR), Right Bundle Branch Block (RBBB) from MIT-BIH arrhythmia database, formation of heart rate time series, extraction of features from RR interval time series, implementation of training algorithm and prediction of arrhythmia classes. Several experiments on the proposed training method are carried out to superior the convergence ability of MLP. The experimental results gives comparably better evaluation over gradient based Back-Propagation (BP) learning algorithm.

show abstract

Application of Particle Swarm Optimization Algorithm to Neural Network Training Process in the Localization of the Mobile Terminal

Cited by 15 publications

References 15 publications

Automatic Detection of Missing Access Points in Indoor Positioning System †

Automatic Detection of Missing Access Points in Indoor Positioning System †

Parameters optimization of deep learning models using Particle swarm optimization

Training of Multilayer Perceptrons with Improved Particle Swarm Optimization for the Heart Diseases Prediction

Contact Info

Product

Resources

About