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ANFIS-Based Signal Reconstruction for Nonlinear Multifunctional Sensor

  • Jian LiuEmail author
  • Yuexin Zhang
  • Chunfeng Li
Original Contribution
  • 16 Downloads

Abstract

In this paper, one approach based on adaptive network-based fuzzy inference system is designed to reconstruct signal for nonlinear multifunctional sensor used in multifunctional sensing technique. Before training, fuzzy rule number is automatically obtained through subtractive clustering to simplify the model structure. Simulation results for an analog circuit model which simulates a nonlinear multifunctional sensor system demonstrate the effectiveness of the designed approach in reconstructing signal. Application of processing real-life data reveals performance of the proposed method is evidently promoted by combining with fuzzy c-means clustering.

Keywords

Multifunctional sensing Signal reconstruction Nonlinearity ANFIS Subtractive clustering 

Notes

Acknowledgements

The authors thank Prof. Guo Wei, Harbin Institute of Technology, for providing the real-life multifunctional sensor data.

Funding

This work is supported in part by the National Natural Science Foundation of China (61201364) and the Fundamental Research Funds for the Central Universities (NS2016034).

References

  1. 1.
    J. Sun, K. Shida, Multilayer sensing and aggregation approach to environmental perception with one multifunctional sensor. IEEE Sens. J. 2(2), 62–72 (2002)CrossRefGoogle Scholar
  2. 2.
    G. Wei, K. Shida, Estimation of concentrations of ternary solution with NaCl and sucrose based on multifunctional sensing technique. IEEE Trans. Instrum. Meas. 55(2), 675–681 (2006)CrossRefGoogle Scholar
  3. 3.
    T.A. Eftimov, W.J. Bock, A simple multifunctional fiber optic level/moisture/vapor sensor using large-core quartz polymer fiber pairs. IEEE Trans. Instrum. Meas. 55(6), 2080–2087 (2006)CrossRefGoogle Scholar
  4. 4.
    A. Flammini, D. Marioli, A. Taroni, Application of an optimal look-up table to sensor data processing. IEEE Trans. Instrum. Meas. 48(4), 813–816 (1999)CrossRefGoogle Scholar
  5. 5.
    D. Liu, J. Sun, G. Wei, X. Liu, Application of moving least squares to multi-sensors data reconstruction. Acta Autom. Sin. 33(8), 823–828 (2007)Google Scholar
  6. 6.
    X. Liu, J. Sun, D. Liu, Nonlinear multifunctional sensor signal reconstruction based on support vector regression. Chin. J. Sens. Actuators 19(4), 1167–1170 (2006)Google Scholar
  7. 7.
    G. Wei, J. Liu, J. Sun, S. Sun, Study on nonlinear multifunctional sensor signal reconstruction method based on LS-SVM. Acta Autom. Sin. 34(8), 869–875 (2008)CrossRefGoogle Scholar
  8. 8.
    J. Liu, G. Wei, J. Sun, Signal reconstruction of nonlinear multifunctional sensor based on B-spline total least squares method. J. Data Acquis. Process. 28(3), 294–300 (2013)Google Scholar
  9. 9.
    J.S.R. Jang, ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)CrossRefGoogle Scholar
  10. 10.
    A. Depari, A. Flammini, D. Marioli, A. Taroni, Application of an ANFIS algorithm to sensor data processing. IEEE Trans. Instrum. Meas. 56(1), 75–79 (2007)CrossRefGoogle Scholar
  11. 11.
    H.F. Kwok, D.A. Linkens, M. Mahfouf, G.H. Mills, Rule-base derivation for intensive care ventilator control using ANFIS. Artif. Intell. Med. 29(3), 185–201 (2003)CrossRefGoogle Scholar
  12. 12.
    P.C. Nayak, K.P. Sudheer, D.M. Rangan, K.S. Ramasastri, A neuro-fuzzy computing technique for modeling hydrological time series. J. Hydrol. 291(1–2), 52–66 (2004)CrossRefGoogle Scholar
  13. 13.
    G.H. Roshani, S.A.H. Feghhi, A. Adineh-Vand, M. Khorsandi, Application of adaptive neuro-fuzzy inference system in prediction of fluid density for a gamma ray densitometer in petroleum products monitoring. Measurement 46, 3276–3281 (2013)CrossRefGoogle Scholar
  14. 14.
    A. Talei, L.H.C. Chua, C. Quek, P.E. Jansson, Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning. J. Hydrol. 488, 17–32 (2013)CrossRefGoogle Scholar
  15. 15.
    J.T. Tsai, K.Y. Chiu, J.H. Chou, Optimal design of SAW gas sensing device by using improved adaptive neuro-fuzzy inference system. IEEE Access 3, 420–429 (2015)CrossRefGoogle Scholar
  16. 16.
    S. Abbaspour, A. Fallah, M. Lindén, H. Gholamhosseini, A novel approach for removing ECG interferences from surface EMG signals using a combined ANFIS and wavelet. J. Electromyogr. Kinesiol. 26, 52–59 (2016)CrossRefGoogle Scholar
  17. 17.
    A.D. Yousif, F.A. Maysam, J. Ali, A new MIMO ANFIS-PSO based NARMA-L2 controller for nonlinear dynamic systems. Eng. Appl. Artif. Intell. 62, 265–275 (2017)CrossRefGoogle Scholar
  18. 18.
    J.E. Sierra, M. Santos, Modelling engineering systems using analytical and neural techniques: hybridization. Neurocomputing 271, 70–83 (2018)CrossRefGoogle Scholar
  19. 19.
    T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. SMC–15(1), 116–132 (1985)CrossRefzbMATHGoogle Scholar
  20. 20.
    H. Ying, Sufficient conditions on uniform approximation of multivariate function by general Takagi-Sugeno fuzzy systems with linear rule consequent. IEEE Trans. Syst. Man Cybern. 28(4), 515–520 (1998)CrossRefGoogle Scholar
  21. 21.
    R.P. Paiva, A. Dourado, B. Duarte, Applying subtractive clustering for neuro-fuzzy modeling of a bleaching plant, inProc. Eur. Control Conf. (Karlsruhe, Germany) (IEEE, 1999) pp. 4497–4502Google Scholar
  22. 22.
    X. Yu, F. Cheng, L. Zhu, Y. Wang, ANFIS modeling based on T-S model and its application for thermal process. Proc. Chin. Soc. Electr. Eng. 26(15), 78–82 (2006)Google Scholar
  23. 23.
    C.C. Kung, C.C. Lin, A new cluster validity criterion for fuzzy c-regression model and its application to T-S fuzzy model identification, in Proc. 2004 IEEE Int. Conf. Fuzzy Syst. (Budapest, Hungary), vol. 3 (IEEE 2004), pp. 1673–1678Google Scholar

Copyright information

© The Institution of Engineers (India) 2019

Authors and Affiliations

  1. 1.Department of Measurement and Testing EngineeringNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China

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