ANFIS Analysis of Wireless Sensor Data with FPGA

Khazal Ahmed; Tuncay Ercan

doi:10.30801/acin.357635

ANFIS Analysis of Wireless Sensor Data with FPGA

Öz

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.

Anahtar Kelimeler

ANFIS,Neuro-Fuzzy System,FPGA,Sensor Nodes,Smart Node

Kaynakça

[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.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Bilgisayar Yazılımı

Bölüm

Araştırma Makalesi

Yazarlar

Khazal Ahmed Bu kişi benim
0000-0001-8171-5582
Türkiye

Tuncay Ercan ^*
0000-0003-0014-5106
Türkiye

Yayımlanma Tarihi

26 Haziran 2018

Gönderilme Tarihi

24 Kasım 2017

Kabul Tarihi

8 Haziran 2018

Yayımlandığı Sayı

Yıl 2018 Cilt: 2 Sayı: 1

DOI

https://doi.org/10.30801/acin.357635

IZ

https://izlik.org/JA68XP52CW

Kaynak Göster

RIS / Bibtex

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

1.Ahmed K, Ercan T. ANFIS Analysis of Wireless Sensor Data with FPGA. ACIN. 2018;2(1):22-32. doi:10.30801/acin.357635

Chicago

Ahmed, Khazal, ve Tuncay Ercan. 2018. “ANFIS Analysis of Wireless Sensor Data with FPGA”. Acta Infologica 2 (1): 22-32. https://doi.org/10.30801/acin.357635.

EndNote

Ahmed K, Ercan T (01 Haziran 2018) ANFIS Analysis of Wireless Sensor Data with FPGA. Acta Infologica 2 1 22–32.

IEEE

[1]K. Ahmed ve T. Ercan, “ANFIS Analysis of Wireless Sensor Data with FPGA”, ACIN, c. 2, sy 1, ss. 22–32, Haz. 2018, doi: 10.30801/acin.357635.

ISNAD

Ahmed, Khazal - Ercan, Tuncay. “ANFIS Analysis of Wireless Sensor Data with FPGA”. Acta Infologica 2/1 (01 Haziran 2018): 22-32. https://doi.org/10.30801/acin.357635.

JAMA

1.Ahmed K, Ercan T. ANFIS Analysis of Wireless Sensor Data with FPGA. ACIN. 2018;2:22–32.

MLA

Ahmed, Khazal, ve Tuncay Ercan. “ANFIS Analysis of Wireless Sensor Data with FPGA”. Acta Infologica, c. 2, sy 1, Haziran 2018, ss. 22-32, doi:10.30801/acin.357635.

Vancouver

1.Khazal Ahmed, Tuncay Ercan. ANFIS Analysis of Wireless Sensor Data with FPGA. ACIN. 01 Haziran 2018;2(1):22-3. doi:10.30801/acin.357635