Romanian Journal of Information Science and Technology (ROMJIST)

An open – access publication

  |  HOME  |   GENERAL INFORMATION  |   ROMJIST ON-LINE  |  KEY INFORMATION FOR AUTHORS  |   COMMITTEES  |  

ROMJIST is a publication of Romanian Academy,
Section for Information Science and Technology

Editor – in – Chief:
Academician Dan Dascalu

Secretariate (office):
Adriana Neagu
Adress for correspondence: romjist@nano-link.net (after 1st of January, 2019)

Editing of the printed version: Mihaela Marian (Publishing House of the Romanian Academy, Bucharest)

Editing of the on-line version: Lucian Milea (University POLITEHNICA of Bucharest)

Sponsors:
• National Institute for R & D
in Microtechnologies
(IMT Bucharest), www.imt.ro
• Association for Generic
and Industrial Technologies (ASTEGI), www.astegi.ro

ROMJIST Volume 21, No. 4, 2018, pp. 377-391, Paper no. 608/2018
 

Dan-Marius DOBREA, Monica-Claudia DOBREA
A MANFIS model of the 3D head position based on a wearable system

ABSTRACT: The fuzzy systems, the artificial neural networks, and the neuro-fuzzy systems have been widely used in modeling of the complex, unknown and nonlinear functions. In this paper, a comparative study between two soft computing techniques was done, namely, between: (a) a Multiple Neuro-Fuzzy Inference System (MANFIS) and (b) two different Artificial Neural Network structures - and this comparison was one investigated in relation with a practical embedded application. More precisely, the research used a new wearable system, named Intell.TieSens, in order to model the complex nonlinear relationship that exist between the capacitive sensors embedded in a tie collar and the 3D head position of the wearer of that tie. The Intell.TieSens system was designed to be a noncontact system, low power, portable, lightweight, having a Bluetooth Low Energy wireless data communication capability in order to be able both to be paired with a personal computer or with a smartphone and to identify in real time the human 3D head position. The results showed the superior performances obtained with MANFIS system – two times better than the performances obtained with the RBF (Radial Basis Function) neural network. Out of the tested models, the MLP (MultiLayer Perceptron) neural network obtained the lowest performances.

KEYWORDS: Fuzzy, MANFIS, CANFIS, neural network, RBF, wearable, capacitive sensing

Read full text (pdf)






  |  HOME  |   GENERAL INFORMATION  |   ROMJIST ON-LINE  |  KEY INFORMATION FOR AUTHORS  |   COMMITTEES  |