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EnglishIP2Cs (Ionic Polymer-Polymer Composites) are electro-active polymers which can be used both as sensors and as actuators. In this work Neural Network models of IP2Cs working as actuators and with water as the solvent are developed using experimental data. Moreover, models proposed here take into account the humidity dependence of the device as modifying input. Three different Neural Network models, i.e. Feed-forward neural network, Radial-basis neural network and recurrent neural network based models are developed and a comparison of proposed model is reported.
Neural modeling of relative humidity on IP2C vibrating transducer / De Luca, V.; Hosseini-Asl, E.; Graziani, S.; Zurada, J. M.. - In: PROCEDIA ENGINEERING. - ISSN 1877-7058. - 87(2014), pp. 424-427. ((Intervento presentato al convegno 28th European Conference on Solid-State Transducers, EUROSENSORS 2014 tenutosi a ita nel 2014.
| Titolo: | Neural modeling of relative humidity on IP2C vibrating transducer | |
| Autori interni: | ||
| Data di pubblicazione: | 2014 | |
| Rivista: | ||
| Citazione: | Neural modeling of relative humidity on IP2C vibrating transducer / De Luca, V.; Hosseini-Asl, E.; Graziani, S.; Zurada, J. M.. - In: PROCEDIA ENGINEERING. - ISSN 1877-7058. - 87(2014), pp. 424-427. ((Intervento presentato al convegno 28th European Conference on Solid-State Transducers, EUROSENSORS 2014 tenutosi a ita nel 2014. | |
| Handle: | http://hdl.handle.net/20.500.11769/323701 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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