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ANFIS Analysis of Wireless Sensor Data with FPGA

Yıl 2018, Cilt: 2 Sayı: 1, 22 - 32, 26.06.2018
https://doi.org/10.30801/acin.357635

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.

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.
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  • [12] Janusz, K. (2002), “Neuro-fuzzy architectures and hybrid learning”, Springer-Verlag,ISBN 978-3-7903-1802-4, Berlin/Heildelberg
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  • [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.
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Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

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

Tuncay Ercan 0000-0003-0014-5106

Yayımlanma Tarihi 26 Haziran 2018
Gönderilme Tarihi 24 Kasım 2017
Yayımlandığı Sayı Yıl 2018 Cilt: 2 Sayı: 1

Kaynak Göster

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