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NEURAL NETWORK BASED MODELING OF NOX DETECTION WITH A SENSOR – POLYMER SYSTEM


CIPRIAN PIULEAC 1, SILVIA CURTEANU 1 *, GABRIELA TELIPAN 2, MARIA CAZACU 3
(1)“GH. ASACHI” TECHNICAL UNIVERSITY, DEPARTMENT OF CHEMICAL ENGINEERING, BD. D. MANGERON NO. 71A, 700050, IAŞI, ROMANIA (2)RESEARCH AND DEVELOPMENT NATIONAL INSTITUTE FOR ELECTRICAL ENGINEERING ICPE CA, SPL. UNIRII 313, SECT. 3, BUCHAREST, ROMANIA (3)“P.

Issue:

SCSCC6, Volume IX, No. 1

Section:

Volume IX, No. 1 (2008)

Abstract:

Some types of synthetic polymers have been tested in nitric oxide sensing. Feed-forward neural networks with different types and topologies are used in mathematical modeling of the system, to predict the voltage of the sensor as function of polymer type, gas concentration and time. In this way, the efficiency of the sensor can be appreciated. The relative errors, extremely low, obtaining in the validation phase, prove the validity of the neural models. The optimization problem performed by inverse neural network modeling answers the question what are the initial conditions that lead to an imposed value for output signal of the sensor.

Keywords:

neural networks, mathematical modeling, polymer-based sensors, direct and inverse modeling, nitric oxide.

Code [ID]:

CSCC6200809V01S01A0005 [0002055]

Full paper:

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