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ANFIS Analysis of Wireless Sensor Data with FPGA

Year 2018, , 22 - 32, 26.06.2018
https://doi.org/10.30801/acin.357635

Abstract

Applications
related with WSNs may include thousands of separate sensor nodes, production
and control data for different industrial sectors. It is important to manage
these applications, monitor the network and reprogram the nodes to avoid
operational problems. In this study, we propose a smart wireless sensor network
using a reconfigurable embedded system of Field-Programmable Gate Arrays
(FPGAs) with a soft-core processor. This processor can be programmed
dynamically and synthesized to implement the preprocessing of sensed data by
ensemble Hybrid Neuro-Fuzzy algorithms such as Adaptive Neuro-Fuzzy Inference
System (ANFIS). The first part of the proposed work is based on Matlab software
to develop and train the ANFIS algorithm. Two different types of data sets
(temperature and humidity) downloaded from Internet have been used in order to
make a comparison between the Matlab Toolbox and modified ANFIS algorithm with
momentum factor. The results obtained in this study have shown that the
modified ANFIS algorithm is the convenient choice in terms of speed, accuracy.

References

  • [1] Ahmed, E.,Mohamed, S., Khaled, M., Ahmed, A., (2016), A hybrid neuro-fuzzy power prediction system for wind energy generation, International Journal of Electrical Power & Energy Systems,74, 384-395.
  • [2] Akyıldız, L., Sankarasubramaniam, Y., Su, W., Cayırcı, E. ,(2002), “Wireless sensor networks: A survey”, Journal of Computer Networks, 38, 393-422.
  • [3] Andrzej, P., Meng, J. ,(2016), “The method of hardware implementation of fuzzy systems on FPGA”, International Conference on Artificial Intelligence and Soft Computing, ICAISC 2016, 284-298.
  • [4] Brassai, S., Hajdu, S., Tamas, T., 4(2015), “Hardware implementation of a neuro-fuzzy controller using high level synthesis tool”, Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics, 10.1515/macro-2015-0018.
  • [5] Brassai, S., Hajdu, Sz., Tamas, T., Bakó, L., (2015), “Hardware implemented adaptive neuro-Fuzzy system”, Carpathian Control Conference, 10.1109/Carpathian CC.2015.7145046.
  • [6] Campo, I., Basterretxea, K., Echanobe, J., (2012), “A System-on-chip development of a neuro–fuzzy embedded agent for ambient-intelligence environments”, IEEE Transactions on Systems, 10.1109/TSMCB.2011.2168516.
  • [7] Cheng-Jian, L., Chun-Cheng, P., (2014), “Classification using an efficient neuro-fuzzy classifier based on adaptive fuzzy reasoning method”, Conference on Computer, Consumer and Control, 10.1109/IS3C.2014.34.
  • [8] Cihan, K.arakuzua, F., Mehmet, A. ,(2016), “FPGA implementation of neuro-fuzzy system with improved PSO learning”, Journal of the International Neural Network Society,79- 2016, 128-140).
  • [9] Doaa, M., Hanaa, T., (2017), “Analysis and design of greenhouse temperature control using adaptive neuro-fuzzy inference system”, Journal of Electrical Systems and Information Technology, 4(1), 34-48.
  • [10] François, Ph., (2014), “Runtime hardware reconfiguration in wireless sensor networks for condition monitoring”, Universitäts - und Landesbibliothek, Darmstadt.
  • [11] Janusz, K., (2000), “Introduction to neuro-fuzzy systems”, Advances in Soft Computing. Springer-Verlag, ISBN 978-3-7903-1852-9. Berlin/Heildelberg.
  • [12] Janusz, K. (2002), “Neuro-fuzzy architectures and hybrid learning”, Springer-Verlag,ISBN 978-3-7903-1802-4, Berlin/Heildelberg
  • [13] Maicon, M.,Luci, P., Silvana, R., Flavia, C., Claudio, M., Paulo F., Albert, Y., (2017), “Damage prediction for wind turbines using wireless sensor and actuator networks”, Journal of Network and Computer Applications. 80,123-140.
  • [14] Meena, S. & Krishna, N., (2014), “Simulation of dynamically reconfigurable wireless sensor node”, International Conference on Electronics and Communication System, Coimbatore, 10.1109/ECS.2014.6892795. India.
  • [15] Melin, P., Soto, J., Castillo, O., Soria, J., (2013), “Time series prediction using ensembles of ANFIS models with genetic optimization of interval type-2 and type-1 fuzzy integrators”, Journal of Hybrid Intelligent Systems, 10.3233/HIS-140196.
  • [16] Potdar, V., Sharif, A., Chang, E., (2009), “Wireless sensor networks: a Survey”, International Conference on Advanced Information Networking and Applications Workshops, 10.1109/WAINA.2009.192. UK, Bradford.
  • [17] Rajasekaran, C., Jeyabharath, R. and Veena, P., (2014), “Hardware-software reconfigurable techniques for wireless sensor network”, Journal of Applied Sciences, Engineering and Technology 8(17): 1855-1862.
  • [18] Rasika, A. and Pathan, M., (2015), “Design and implementation of ANFIS algorithm using VHDL for vehicular system”, Journal on Recent and Innovation Trends in Computing and Communication 3, 2: 820-824.
  • [19] Sándor, T., Szabolcs, H., Tibor, T., (2015), “Embedded adaptive neuro-fuzzy inference system with hardware implemented real time parameter update”, International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics, 10.1515/macro-2015-0021
  • [20] Taylor, J., Buizza, R., (2002), “Neural network load forecasting with weather ensemble predictions”, IEEE Transactions on Power Systems, 10.1109/TPWRS.2002.800906.
Year 2018, , 22 - 32, 26.06.2018
https://doi.org/10.30801/acin.357635

Abstract

References

  • [1] Ahmed, E.,Mohamed, S., Khaled, M., Ahmed, A., (2016), A hybrid neuro-fuzzy power prediction system for wind energy generation, International Journal of Electrical Power & Energy Systems,74, 384-395.
  • [2] Akyıldız, L., Sankarasubramaniam, Y., Su, W., Cayırcı, E. ,(2002), “Wireless sensor networks: A survey”, Journal of Computer Networks, 38, 393-422.
  • [3] Andrzej, P., Meng, J. ,(2016), “The method of hardware implementation of fuzzy systems on FPGA”, International Conference on Artificial Intelligence and Soft Computing, ICAISC 2016, 284-298.
  • [4] Brassai, S., Hajdu, S., Tamas, T., 4(2015), “Hardware implementation of a neuro-fuzzy controller using high level synthesis tool”, Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics, 10.1515/macro-2015-0018.
  • [5] Brassai, S., Hajdu, Sz., Tamas, T., Bakó, L., (2015), “Hardware implemented adaptive neuro-Fuzzy system”, Carpathian Control Conference, 10.1109/Carpathian CC.2015.7145046.
  • [6] Campo, I., Basterretxea, K., Echanobe, J., (2012), “A System-on-chip development of a neuro–fuzzy embedded agent for ambient-intelligence environments”, IEEE Transactions on Systems, 10.1109/TSMCB.2011.2168516.
  • [7] Cheng-Jian, L., Chun-Cheng, P., (2014), “Classification using an efficient neuro-fuzzy classifier based on adaptive fuzzy reasoning method”, Conference on Computer, Consumer and Control, 10.1109/IS3C.2014.34.
  • [8] Cihan, K.arakuzua, F., Mehmet, A. ,(2016), “FPGA implementation of neuro-fuzzy system with improved PSO learning”, Journal of the International Neural Network Society,79- 2016, 128-140).
  • [9] Doaa, M., Hanaa, T., (2017), “Analysis and design of greenhouse temperature control using adaptive neuro-fuzzy inference system”, Journal of Electrical Systems and Information Technology, 4(1), 34-48.
  • [10] François, Ph., (2014), “Runtime hardware reconfiguration in wireless sensor networks for condition monitoring”, Universitäts - und Landesbibliothek, Darmstadt.
  • [11] Janusz, K., (2000), “Introduction to neuro-fuzzy systems”, Advances in Soft Computing. Springer-Verlag, ISBN 978-3-7903-1852-9. Berlin/Heildelberg.
  • [12] Janusz, K. (2002), “Neuro-fuzzy architectures and hybrid learning”, Springer-Verlag,ISBN 978-3-7903-1802-4, Berlin/Heildelberg
  • [13] Maicon, M.,Luci, P., Silvana, R., Flavia, C., Claudio, M., Paulo F., Albert, Y., (2017), “Damage prediction for wind turbines using wireless sensor and actuator networks”, Journal of Network and Computer Applications. 80,123-140.
  • [14] Meena, S. & Krishna, N., (2014), “Simulation of dynamically reconfigurable wireless sensor node”, International Conference on Electronics and Communication System, Coimbatore, 10.1109/ECS.2014.6892795. India.
  • [15] Melin, P., Soto, J., Castillo, O., Soria, J., (2013), “Time series prediction using ensembles of ANFIS models with genetic optimization of interval type-2 and type-1 fuzzy integrators”, Journal of Hybrid Intelligent Systems, 10.3233/HIS-140196.
  • [16] Potdar, V., Sharif, A., Chang, E., (2009), “Wireless sensor networks: a Survey”, International Conference on Advanced Information Networking and Applications Workshops, 10.1109/WAINA.2009.192. UK, Bradford.
  • [17] Rajasekaran, C., Jeyabharath, R. and Veena, P., (2014), “Hardware-software reconfigurable techniques for wireless sensor network”, Journal of Applied Sciences, Engineering and Technology 8(17): 1855-1862.
  • [18] Rasika, A. and Pathan, M., (2015), “Design and implementation of ANFIS algorithm using VHDL for vehicular system”, Journal on Recent and Innovation Trends in Computing and Communication 3, 2: 820-824.
  • [19] Sándor, T., Szabolcs, H., Tibor, T., (2015), “Embedded adaptive neuro-fuzzy inference system with hardware implemented real time parameter update”, International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics, 10.1515/macro-2015-0021
  • [20] Taylor, J., Buizza, R., (2002), “Neural network load forecasting with weather ensemble predictions”, IEEE Transactions on Power Systems, 10.1109/TPWRS.2002.800906.
There are 20 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Makaleler
Authors

Khazal Ahmed This is me 0000-0001-8171-5582

Tuncay Ercan 0000-0003-0014-5106

Publication Date June 26, 2018
Submission Date November 24, 2017
Published in Issue Year 2018

Cite

APA Ahmed, K., & Ercan, T. (2018). ANFIS Analysis of Wireless Sensor Data with FPGA. Acta Infologica, 2(1), 22-32. https://doi.org/10.30801/acin.357635
AMA Ahmed K, Ercan T. ANFIS Analysis of Wireless Sensor Data with FPGA. ACIN. June 2018;2(1):22-32. doi:10.30801/acin.357635
Chicago Ahmed, Khazal, and Tuncay Ercan. “ANFIS Analysis of Wireless Sensor Data With FPGA”. Acta Infologica 2, no. 1 (June 2018): 22-32. https://doi.org/10.30801/acin.357635.
EndNote Ahmed K, Ercan T (June 1, 2018) ANFIS Analysis of Wireless Sensor Data with FPGA. Acta Infologica 2 1 22–32.
IEEE K. Ahmed and T. Ercan, “ANFIS Analysis of Wireless Sensor Data with FPGA”, ACIN, vol. 2, no. 1, pp. 22–32, 2018, doi: 10.30801/acin.357635.
ISNAD Ahmed, Khazal - Ercan, Tuncay. “ANFIS Analysis of Wireless Sensor Data With FPGA”. Acta Infologica 2/1 (June 2018), 22-32. https://doi.org/10.30801/acin.357635.
JAMA Ahmed K, Ercan T. ANFIS Analysis of Wireless Sensor Data with FPGA. ACIN. 2018;2:22–32.
MLA Ahmed, Khazal and Tuncay Ercan. “ANFIS Analysis of Wireless Sensor Data With FPGA”. Acta Infologica, vol. 2, no. 1, 2018, pp. 22-32, doi:10.30801/acin.357635.
Vancouver Ahmed K, Ercan T. ANFIS Analysis of Wireless Sensor Data with FPGA. ACIN. 2018;2(1):22-3.