A Review on ANFIS based Linearization of Non Linear Sensors



Low cost sensors having high sensitivity, better resolution and linear characteristics are required for industrial applications based on instrumentation and control. Unfortunately, the natural non linear characteristic of sensor itself and also the dynamic nature of the environment, aging effect, inherent sensor’s noise and data loss due to transients or intermittent faults affects the sensor characteristics non linearly. As the transfer characteristic of most sensors is nonlinear in nature, obtaining data from such a nonlinear sensor, by using an optimized device, has always been a design challenge. Linearization of nonlinear sensor characteristic in digital environment, is a vital step in the instrument signal conditioning process. This paper gives a brief review about how to overcome this nonlinear characteristic of the sensor using artificial intelligence such as  Hybrid Neuro Fuzzy Logic (HNFL) based on digital linearization technique using VLSI technology such as Field Programmable Gate Array (FPGA).

Full Text:



Alaa Abdul Hussein Salman , Fadhil Rahma Tahir, Mofeed Turky Rashid, ―Design and implementation model for linearization sensor characteristic by FPAA, Iraq J. Electrical and Electronic Engineering, Vol. 11 No.2, 2015.

Om Prakash Verma, Rajesh Singla, Rajesh Kumar, ―Intelligent temperature controller for water bath system‖, International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 6, No. 9, pp. 1178-1184, 2012.

G. Bucci, M. Faccio and C. Landi, ―New ADC with piecewise linear characteristic: case study – implementation of a smart humidity sensor‖, IEEE Trans, Instrumentation and Measurement, vol. 49, No. 6, pp. 1154-1166, Dec. 2000.

C. Alippi, A. Ferrero and V. Piuri, ―Artificial intelligence for instruments and measurement applications‖, IEEE Instrumentation and Measurement Magazine, vol. 1, No. 1, pp. 9-17, March 1998.

H.J. Hoge, “Comparison of circuits for linearizing the temperature indications of thermistors,” Rev. Sci. Instrum., vol. 50, no. 3, pp. 316-320, Mar. 1979.

A. Lopez-Martin, Mikel Zuza and A. Carlosena, “A CMOS A/D converter with piecewise linear characteristics and its application to sensor linearization,” Analog Integr. Cir. Sig. Proc., vol. 36, no. 1-2, pp. 39-46, Jul. 2003.

A. Häberli, M. Schneider, P. Malcovati, R. Castagnetti, F. Maloberti and H. Baltes, “Two-dimensional magnetic microsensor with on-chip signal processing for contactless angle measurement,” IEEE Journal of Solid-State Circuits, vol. 31, no. 12, pp. 1902-1907, Dec. 1996.

Antonio J., Lopez-Martin, Alfonso Carlosena, ―Sensor signal linearization tecqniques : A comparative analysis‖, IEEE Fourth Latin American Symposium on Circuits and Systems (LASCAS), pp. 1-4, March 2013.

Mounir Bouhedda, ―Neuro- Fuzzy sensor’s linearization based FPGA, IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), Volume. 01, Pages. 324 –328, Sept. 2013.

S. N. Engin, J. Kuvulmaz and V. E. Ömurlü, “Fuzzy control of an ANFIS model representing a nonlinear liquid-level system,” Neural Computing & Applications, Volume 13, Issue 3, pp 202-210, Sept. 2004.

Jyh-Shing Roger Jang, “ANFIS: adpative network based inference system,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, N°3, May/June 1993.

A. Volnei Pedroni, Circuit design with VHDL, MIT Press, 2004.

H. S. Ng, K.P. Lam, Analog and digital FPGA implementation of BRIN for optimization problems, IEEE Trans, Neural Networks, vol. 14, No. 5, pp. 1413-1425, Sept. 2003.

A. Sachenko, V. Kochan and V. Turchenko, Instrumentation for Gathering Data, IEEE Instrumentation and Measurement Magazine, vol. 6, No. 3, pp. 34-40, Sept. 2003.

P. Arpaia, P. Daponte, D. Grimaldi and L. Michaeli, ANN – Based Error Reduction for Experimentally Modeled Sensors, IEEE Trans, Instrumentation and Measurement, vol. 51, No. 1, pp. 23-29, Feb. 2002.

N. N. Charniya, Some Features of Neural Networks based Intelligent Sensors and Design Issues, International Journal of Computer Applications, vol. ICCIA, No.2, pp. 1-4, March 2012.

D. Patranabis, S. Gosh, C. Bakshi, “Linearizing Transduser characteristics,” IEEE Trans. Instrum. Meas., Vol. 37, 1988, pp. 66-69.

D.K. Anvekar, B.S. Sond, “Transducer output signal processing using dual and triple microprocessor systems,” IEEE Trans. Instrum. Meas., Vol. 38, 1989, pp. 834-836.

P.N. Mahana, F.N. Trofimenkoff, “Transducer output signal proceesing using an eight-bit microcomputer,” IEEE Trans. Instrum. Meas., Vol. 35, 1986, pp. 182-186.

A. Flammini, D. Marioli, A. Taroni, “Transducer output signal processing using an optimal look-up table in microcontrollerbased systems,” Electron. Lett., Vol. 33, 1997, pp 1197-1198.


  • There are currently no refbacks.


The “ADBU Journal of Engineering Technology (AJET)" ISSN:2348-7305

This journal is published under the terms of the Creative Commons Attribution (CC-BY) (http://creativecommons.org/licenses/)

Number of Visitors to this Journal:web counter