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Machine Olfaction, Signal Processing and Pattern Recognition

[Multilayer Perceptron in Electronic Nose]

Paper:

Lei Zhang* and Fengchun Tian, Performance Study of Multilayer Perceptrons in a Low-Cost Electronic Nose, IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 7, pp. 1670-1679, 2014. [paper]

Abstract:

Nonselective gas sensor array has different sensitivities to different chemicals in which each gas sensor will also produce different voltage signals when exposed to an analyte with different concentrations. Therefore, the characteristics of cross sensitivities and broad spectrum of nonselective chemical sensors promote the fast development of portable and low-cost electronic nose (E-nose). Simultaneous concentration estimation of multiple kinds of chemicals is always a challengeable task in E-nose. Multilayer perceptron (MLP) neural network, as one of the most popular pattern recognition algorithms in E-nose, has been studied further in this paper. Two structures of single multiple inputs multiple outputs (SMIMO) and multiple multiple inputs single output (MMISO)-based MLP with parameters optimization in neural network learning processing using eight computational intelligence optimization algorithms are presented in this paper for detection of six kinds of indoor air contaminants. Experiments prove that the performance in accuracy and convergence of MMISO structure-based MLP are much better than SMIMO structure in concentration estimation for more general use of E-nose.

Signal Response

Approach:

Experimental Results

Estimation performance comparisons of SMIMO and MMISO structure using 12 methods. Index of methods: (1) MLR, (2) PLS, (3) PCR, (4) single BP, (5) SGA-BP, (6) SPSO-BP, (7) IWAPSO-BP, (8) APSO-BP, (9) DRPSO-BP, (10) ARPSO-BP, (11) PSOBC-BP, and (12) PSOAGS-BP. Prediction of chemicals: (a) formaldehyde, (b) benzene, (c) toluene, (d) carbon monoxide, (e) ammonia, and (f) nitrogen dioxide.

References:

[1] L. Zhang, F. Tian, C. Kadri, G. Pei, H. Li, and L. Pan, “Gases concentration estimation using heuristics and bio-inspired optimization models for experimental chemical electronic nose,” Sensors Actuat. B, Chem., vol. 160, no. 1, pp. 760–770, 2011.

[2] D. Gao, Z. Yang, C. Cai, and F. Liu, “Performance evaluation of multilayer perceptrons for discriminating and quantifying multiple kinds of odors with an electronic nose,” Neural Netw., vol. 33, pp. 204–215, Sep. 2012.

[3] K. Brudzewski, S. Osowski, and A. Dwulit, “Recognition of coffee using differential electronic nose,” IEEE Trans. Instrum. Meas., vol. 61, no. 6, pp. 1803–1810, Jun. 2012.