Different strategies for the identification of gas sensing systems

Abstract We have shown that it is possible to extract dynamical models for non-linear gas sensors from experimental input-output data. Seven different methods (two linear and five non-linear) have been evaluated in terms of their prediction performance on the sensor response to white gaussian inputs. Two methods, artificial neural networks and modified Wiener kernels estimated by least squares, show very low prediction errors with models extracted from only 300 input-output data pairs. A detailed discussion on the advantages and disadvantages of very method is presented.

[1]  Hai-Wen Chen,et al.  Modeling and identification of parallel nonlinear systems: structural classification and parameter estimation methods , 1995, Proc. IEEE.

[2]  K. Naka,et al.  Identification of multi-input biological systems. , 1974, IEEE transactions on bio-medical engineering.

[3]  Y. W. Lee,et al.  Measurement of the Wiener Kernels of a Non-linear System by Cross-correlation† , 1965 .

[4]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[5]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[6]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[7]  H. Akaike.  Fitting autoregressive models for prediction , 1969 .

[8]  Udo Weimar,et al.  Polymer-based sensor arrays and multicomponent analysis for the detection of hazardous oragnic vapours in the environment , 1995 .

[9]  Corrado Di Natale,et al.  Pattern recognition in gas sensing: well-stated techniques and advances , 1995 .

[10]  Stephen A. Billings,et al.  Properties of neural networks with applications to modelling non-linear dynamical systems , 1992 .

[11]  H. Akaike.  A new look at the statistical model identification , 1974 .

[12]  Leon O. Chua,et al.  Measuring Volterra kernels , 1983 .

[13]  Vito Volterra,et al.  Theory of Functionals and of Integral and Integro-Differential Equations , 2005 .

[14]  Keith R. Godfrey,et al.  Correlation methods , 1980, Autom..

[15]  C. D. Geisler,et al.  Artifacts in Wiener Kernels Estimated Using Gaussian White Noise , 1984, IEEE Transactions on Biomedical Engineering.

[16]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[17]  S. Billings,et al.  Correlation based model validity tests for non-linear models , 1986 .

[18]  C. Di Natale,et al.  Different Strategies For The Dynamical Calibration Of Gas Sensors , 1995, Proceedings of the International Solid-State Sensors and Actuators Conference - TRANSDUCERS '95.

[19]  R. Haber,et al.  Parametric identification of nonlinear dynamic systems based on nonlinear crosscorrelation functions , 1988 .

[20]  Giorgio Sberveglieri,et al.  A systematic investigation on the use of time-dependent sensor signals in signal-processing techniques , 1995 .

[21]  N. Wiener,et al.  Nonlinear Problems in Random Theory , 1964 .

[22]  Wolfgang Rosenstiel,et al.  Application of neural-network systems to the dynamic response of polymer-based sensor arrays , 1995 .

[23]  Udo Weimar,et al.  Structure identification of non-linear models for QMB polymer-coated sensors , 1995 .