Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloComparison of Fisher's linear discriminant to multilayer perceptron networks in the classification of vapors using sensor array data
Anno di pubblicazione2006
FormatoElettronico
Autore/iPardo, M; Sisk, BC; Sberveglieri, G; Lewis, NS
Affiliazioni autoriCALTECH, Div Chem & Chem Engn, Pasadena, CA 91125 USA; Univ Brescia, Dept Chem & Phys Mat, I-25133 Brescia, Italy; Univ Brescia, CNR, INFM, SENSOR Lab, I-25133 Brescia, Italy
Autori CNR e affiliazioni
  • GIORGIO SBERVEGLIERI
  • MATTEO PARDO
Lingua/e
  • inglese
AbstractTwo different classification methods, Fisher's linear discrimmant (FLD) and a multilayer perceptron neural network (NILP), were directly compared with respect to their abilities to differentiate response patterns arising from arrays of chemical vapor detectors. The algorithms were compared in five different types of tasks that had been selected because they produced classification problems of varying character and difficulty. In one task. an array of 20 compositionally distinct carbon black-polymer composite vapor detectors was exposed to P/P-0= 0.0075 1-propanol and P/P-0 = 0.0083 2-propanol, where P and P-0 are the partial pressure and standard vapor pressure, respectively, of a given analyte. The second task consisted of classification of a mixture of P/P-0 = 0.011 1-propanol and P/P-0 = 0.0090 2-propanol versus a mixture of P/P-0 = 0.0090 1-propanol and P/P-0 = 0.011 2-propanol. A third task consisted of multiple concentrations of three hydrocarbons, and a fourth task involved clustering two hydrocarbons in the presence of a variable background composition. An additional dataset was generated by exposing an array of five thin-film metal-oxide sensors to the headspace of seven different coffee blends. In each case, the NILP and FLD techniques were compared using the 5-sensor subset of the 20 available sensors that proved optimal for that dataset. The FLD and MLP algorithms yielded comparable performance on straightforward classification tasks, whereas the NILP technique yielded better performance on tasks that involved non-linear classification boundaries. In addition. for the four datasets produced by the carbon black-polymer composite detector array, the performance of each possible 5-sensor subset was evaluated using both signal processing approaches. The performance of the best 5-sensor subset selected with NILP was found to be slightly better than the performance of the FLD-selected subsets, and the performance of the median 5-sensor subset using MLP was nearer to that of the optimal subset than the median sensor array selected by FLD. In one case, the optimal test set performance distribution was found to be significantly better with NILP than with FLD: MLP had a clear advantage (86% versus 57% correct classification rate) when applied to the 'coffees' dataset. and this trend is likely applicable to other multi-cluster classification tasks that consisted of non-Gaussian shaped data in lower-dimensional spaces. (c) 2005 Published by Elsevier B.V.
Lingua abstract-
Altro abstract-
Lingua altro abstract-
Pagine da647
Pagine a655
Pagine totali-
RivistaSensors and actuators. B, Chemical (Print)
Attiva dal 1990
Editore: Elsevier Sequoia - Lausanne
Paese di pubblicazione: Svizzera
Lingua: inglese
ISSN: 0925-4005
Titolo chiave: Sensors and actuators. B, Chemical (Print)
Titolo proprio: Sensors and actuators. (Print)
Titolo abbreviato: Sens. actuators, B, Chem. (Print)
Titolo alternativo: Sensors and actuators B (Print)
Numero volume della rivista115
Fascicolo della rivista-
DOI10.1016/j.snb.2005.10.033
Verificato da refereeSì: Internazionale
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000237761000013)
Parole chiavePATTERN-RECOGNITION, Artificial neural networks, Fisher's linear discriminant, chemical sensor arrays
Link (URL, URI)-
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • INFM — Centro di responsabilità scientifica INFM
Moduli/Attività/Sottoprogetti CNR
  • MD.P05.015.001 : Materiali nanostrutturati di ossidi metallici e altri semicondutttori per la sensoristica e applicazioni avanzate
Progetti Europei-
Allegati
Comparison of Fisher's linear discriminant to multilayer perceptron networks in the classification of vapors using sensor array data (documento privato )
Tipo documento: application/pdf

Dati storici
I dati storici non sono modificabili, sono stati ereditati da altri sistemi (es. Gestione Istituti, PUMA, ...) e hanno solo valore storico.
Area disciplinarePhysics
Area valutazione CIVRScienze fisiche
Rivista ISISENSORS AND ACTUATORS B-CHEMICAL [09220J0]