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Deep Neural Networks for Image-Based Dietary Assessment

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A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks.

Jaroslaw Sadowski¹, Jacek Stefanski¹

¹Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland.

Sensors (Basel, Switzerland)

|January 23, 2024

Summary

This summary is machine-generated.

This study shows that simple feedforward neural networks (FNNs) can ensure iterative location estimation algorithms always converge. Minimal FNNs with a single hidden layer are sufficient for effective position estimation.

Keywords:

feedforward neural network iterative algorithms position estimation radio localization

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Area of Science:

* Signal Processing
* Artificial Intelligence
* Network Engineering

Background:

* Iterative location estimation algorithms often face convergence challenges.
* Selecting optimal starting points is crucial for algorithm performance.
* Existing methods may not guarantee convergence in all scenarios.

Purpose of the Study:

* To determine the minimum feedforward neural network (FNN) size for guaranteed convergence of iterative location estimation.
* To investigate the effectiveness of FNNs in supporting 2D and 3D position estimation.
* To analyze the impact of FNN parameters on convergence probability and iteration count.

Main Methods:

* Utilizing a feedforward neural network (FNN) to select initial points for iterative location algorithms.
* Designing and evaluating various FNN structures for 2D and 3D positioning.
* Simulating performance in a Time Difference of Arrival (TDoA) positioning network.

Main Results:

* FNNs achieved a 100% convergence probability for iterative algorithms in a TDoA network.
* Simple FNNs with one hidden layer and a dozen neurons were sufficient.
* Data on average and maximum iteration counts were obtained, indicating FNN effectiveness.

Conclusions:

* Feedforward neural networks effectively solve the convergence problem in iterative location estimation.
* Minimal neural network architectures are adequate for enhancing positioning accuracy and reliability.
* FNNs offer a promising approach to improve the performance of positioning systems.