Machine learning approach to point localization system

The article introduces point localization systems in 3D Euclidean space based on neural networks. There are two models presented. The first one identified distances between a randomly generated point and a reference points in the defined domain. Then a neural network uses the obtained distances as its inputs to determine the actual position of the point in the domain space. Due to a relatively good accuracy that was obtained during the experimental study, the proposed model based on neural networks was used in the second model as an acoustic Motion Capturing system (MoCap). MoCap system is represented by a neural network that uses obtained distances between transmitters and a receiver as its inputs to determine an actual position of the receiver in space. We also propose a new way to minimize a training set by using ANFIS approach in this specific problem. All obtained results are summarized in the conclusion.