A time delay neural network for estimation of gas concentrations in a mixture

On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario

“…a calibration relying on multiple sensor responses, gas multi-sensor devices have shown interesting capabilities for dealing with concentration estimation problems in complex mixtures. Often, the selected calibration model has been artificial neural networks [9][10][11], partial least squares (PLS) [12,13] and more recently Kernel methods like support vector machines [14,15]. Their capability to exploit partial selectivity of sensors as an advantage can give them a chance of well performing in modelling this complex framework.…”

A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems

“…Then, the cleaning phase base frequency shifts (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) were subtracted from sensor responses. After this process steady state sensor response shifts were calculated and these data were linearly normalized between 0.1 and 0.9.…”

“…Implementation of NN to analyse the response of gas sensor arrays offers several advantages over conventional signal processing in terms of adaptability, noise tolerance, etc. [4,5,[8][9][10].…”

Quantitative discrimination of the binary gas mixtures using a combinational structure of the probabilistic and multilayer neural networks

“…Implementation of the neural networks to analyse the responses of gas sensor arrays offers several advantages over conventional signal processing in terms of adaptability, noise tolerance, etc. [2,[6][7][8][9][10]. The MLNN consists of fully interconnected layers of simple and identical processing units called neurons.…”