The week's pick
Random Articles
Reseach Article
A Systematic Approach of Data Fusion Technique in RFID Sensor Network using Neuro-Fuzzy Technique
by
Sujata Kundu,
Chayan Ranjit
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 139 - Number 9 |
| Year of Publication: 2016 |
| Authors: Sujata Kundu, Chayan Ranjit |
10.5120/ijca2016909045
|
Sujata Kundu, Chayan Ranjit . A Systematic Approach of Data Fusion Technique in RFID Sensor Network using Neuro-Fuzzy Technique. International Journal of Computer Applications. 139, 9 ( April 2016), 7-14. DOI=10.5120/ijca2016909045
@article{
10.5120/ijca2016909045,
author = {
Sujata Kundu,
Chayan Ranjit
},
title = { A Systematic Approach of Data Fusion Technique in RFID Sensor Network using Neuro-Fuzzy Technique },
journal = {
International Journal of Computer Applications
},
issue_date = { April 2016 },
volume = { 139 },
number = { 9 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = {
7-14
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume139/number9/24516-2016909045/
},
doi = { 10.5120/ijca2016909045 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:40:27.716036+05:30
%A Sujata Kundu
%A Chayan Ranjit
%T A Systematic Approach of Data Fusion Technique in RFID Sensor Network using Neuro-Fuzzy Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 139
%N 9
%P 7-14
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In this paper, a systematic approach is used for sale prediction in a multistoried retail business with the help of multi sensor data fusion technique using Neural Network and Fuzzy Logic. This method can better solve problems existing in traditional sale prediction which are basically depends on the personal experience. In this work a 3-layers data fusion structure is used. In this system, the sale data experiential characteristic and the sale data-fitting characteristic are fused by fuzzy inference system to get sale prediction. After using the Feed Forward Back Propagation algorithm, the system is trained for predefined target value and then the system calculate the sale statistic in runtime which is fused with the data of expert databases using fuzzy logic technique.
References
- M. Demirbas, “Wireless sensor networks for monitoring of large public buildings,” Department of large public buildings,” Department of Buffalo, SUNY Buffalo, NY, Tech. Rep., 2005
- D. Hall and A. Garga, “Pitfalls in Data Fusion (and How to Avoid Them)”, Proceedings of the Second International Conference on Information Fusion (Fusion ’99), pp. 429-436, 1999.
- P. Manjunatha, A.K. Verma and A. Srividya, “Multi-Sensor Data Fusion in Cluster based Wireless Sensor Networks Using Fuzzy Logic Method”, IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, INDIA December 8-10.,2008
- Y. Simon, “A fuzzy logic approach to fire detection in aircraft dry bays and engine compartments,” IEEE Transaction on Industrial Electronics, vol. 47, no. 5, pp. 1161–1171, Oct 2000
- Y.C. Ho, (1964) A Bayesian approach to problems in stochastic estimation and control, IEEE Trans. Automatic Control AC-9, 333
- J. Braun, (2000) Dempster-Shafer theory and Bayesian reasoning in multisensor data fusion, Sensor Fusion: Architectures, Algorithms and Applications IV; Proceedings of SPIE 4051, 255–266
- D.J. Kewley, (1992) Notes on the use of Dempster-Shafer and Fuzzy Reasoning to fuse identity attribute data, Defense Science and Technology Organization, Adelaide. Technical memorandum SRL-0094-TM.
- M. A. Simard, and et al. (2000) Multisource information fusion applied to ship identification for the Recognized Maritime Picture, Sensor Fusion: Architectures, Algorithms and Applications IV; Proceedings of SPIE 4051, 67–78
- S. Schwartz, (2000) Algorithm for automatic recognition of formations of moving targets, Sensor Fusion: Architectures, Algorithms and Applications IV; Proceedings of SPIE 4051, 407–417
- J. Triesch, (2000) Self-organized integration of adaptive visual cues for face tracking, Sensor Fusion: Architectures, Algorithms and Applications IV; Proceedings of SPIE 4051, 397–406
- F. Cremer, and et al. (October 1998) Sensor data fusion for antipersonnel land mine detection, Proceedings of EuroFusion98, 55–60
- M. Cooper, M. Miller, (1998) Information gain in object recognition via sensor fusion, Proceedings of the International Conference on Multisource-Multisensor Information Fusion (Fusion ’98), 1, 143–148
- D. Str¨omberg, (2000) A multi-level approach to sensor management, Sensor Fusion: Architectures, Algorithms and Applications IV; Proceedings of SPIE 4051, 456–461
- H. Myler, (2000) Characterization of disagreement in multiplatform and multisensor fusion analysis, Signal Processing, Sensor Fusion, and Target Recognition IX; Proceedings of SPIE 4052, 240–248
- Yi Zou, Ho Yeong Khing, Chua Chin Seng, Zhou Xiao Wei, (2000) Multi-ultrasonic sensor fusion for autonomous mobile robots, Sensor Fusion: Architectures, Algorithms and Applications IV; Proceedings of SPIE 4051, 314–321
- M. Kokar, and et al (2000) A reference model for data fusion systems, Sensor Fusion: Architectures, Algorithms and Applications IV; Proceedings of SPIE 4051, 191–202
- Z. B. Li and H. Zhou, “Research on the application of fuzzy data fusion to cable fire detecting system,” in Proceedings of the Third International Conference on Machine Learning and Cybernetics, vol. 4, Shanghai, Aug 2004, pp. 2083– 2085
- H. Bao, J. Li, X. Zeng, and J. Zhang, “A fire detection system based on intelligent data fusion technology,” in Proceedings of the Third International Conference on Machine Learning and Cybernetics, vol. 2, Nov 2003, pp. 1096– 1101
- M. Marin-Perianu and P. Havinga, D-FLER A Distributed Fuzzy Logic Engine for Rule-Based Wireless Sensor Networks, ser. Lecture Notes in Computer Science. Berlin / Heidelberg: Springer, Nov 2007, vol. 4836/2007, pp. 86– 101
- I. Chair and P. Varshney, “Optimal data fusion of correlated local decisions in multiple sensor detection systems,” IEEE Transactions on AES, vol. 28, no. 3, pp. 916–920, 1992.
- A. Orsoni et al.,“ Data Fusion for Trend Identification in Large Retail Business using Fuzzy Technique”, 14th European Simulation Symposium A. Verbraeck, W. Krug, eds. (c) SCS Europe BVBA, 2002
- H. Bao, et al., “A Fire Detection System Based on Intelligent Data Fusion Technology”, the Second International Conference on Machine Learning and Cybernetics, Xi", 2-5 November 2003.
- F. Campos, et al., “An Extended Approach of Dempster-Shafer Theory”, IEEE Explore, April 2009.
- T. Garma, et al., “Vision and RFID data fusion for tracking people in crowds by a mobile robot”, Comput. Vis. Image Understand.(2010), doi:10.1016/j.cviu.2010.01.008
- S. Kundu, S. Chowdhury, S. Bhattacharyaa (2016) “Analyzing Different Features of Artificial Neural Networks and Its Applications In Different Fields”.
- S. N. Sivanandam, S. Sumathi, S.N. Deepa. ”Introduction to Neural Network”,Tata McGraw Hill.
- D. Hush, B. Horne (Jan. 1993) Progress in supervised neural networks: “what’s new since Lippman?”, IEEE Signal Processing Magazine, pp. 8–39
- http://www.mathworks.com/ Fuzzy Logic Toolbox user’s guide.
Index Terms
Computer Science
Information Sciences
Keywords