Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

A belief-rule-based model for information fusion with insufficient multi-sensor data and domain knowledge using evolutionary algorithms with operator recommendations

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Multi-sensor information fusion (IF) has attracted the attention of many researchers in different fields because it can improve modeling accuracy by integrating information gathered from multiple sensors. Traditionally, the inputs of the multi-sensor IF problem are mostly quantitative data. However, to provide a more comprehensive decision support, both quantitative data and experts’ domain knowledge should be synthesized in the IF process. Moreover, there may be insufficient data in many practical conditions, which would make many conventional approaches inapplicable. Because the belief rule base (BRB) has shown advantages in nonlinear modeling with insufficient data and experts’ domain knowledge, a BRB-IF model is proposed for the multi-sensor IF problem. To improve its efficiency, an optimization model and the corresponding optimization algorithm for BRB-IF are proposed. The particle swarm optimization algorithm and differential evolutionary algorithm are tested as the optimization engine to solve the optimization model with the operator recommendation strategy. The efficiency of the proposed BRB-IF is validated by a practical case study of threat level assessment, where a comparison between BRB-IF and the neural network is conducted.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Ahlberg S, Horling P, Johansson K et al (2007) An information fusion demonstrator for tactical intelligence processing in network-based defense. Inform Fusion 8:84–107

    Article  Google Scholar 

  • Bachmann C, Abdulhai B, Roorda MJ et al (2013) A comparative assessment of multi-sensor data fusion techniques for freeway traffic speed estimation using microsimulation modeling. Transp Res Part C Emerg Technol 26:33–48

    Article  Google Scholar 

  • Banerjee TP, Das S (2012) Multi-sensor data fusion using support vector machine for motor fault detection. Inform Sci 217:96–107

    Article  Google Scholar 

  • Bigdeli B, Samadzadegan F, Reinartz P (2014) Feature grouping-based multiple fuzzy classifier system for fusion of hyperspectral and LIDAR data. J Appl Remote Sens 8:1–15

    Article  Google Scholar 

  • Bouchon-Meunier B (2013) Aggregation and fusion of imperfect information. Physica. Springer, Berlin

    MATH  Google Scholar 

  • Cai B, Liu Y, Fan Q (2014) Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network. Applied Eng 114:1–9

    Article  Google Scholar 

  • Carpenter GA, Martens S, Ogas OJ (2005) Self-organizing information fusion and hierarchical knowledge discovery: a new framework using ARTMAP neural networks. Neural Netw 18:287–295

    Article  Google Scholar 

  • Chang LL, Sun JB, Jiang J, Li MJ (2015) Parameter learning for the belief rule base system in the residual life probability prediction of metalized film capacitor. Knowl Based Syst 73:69–80

    Article  Google Scholar 

  • Clere M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6:58–73

    Article  Google Scholar 

  • D’Aniello G, Gaeta M, Hong TP (2017) Effective quality-aware sensor data management. IEEE Trans Emerg Top Comput Intell. https://doi.org/10.1109/TETCI.2017.2782800

    Google Scholar 

  • Daniello G, Lola V, Orciuoli F (2016) Employing fuzzy consensus for assessing reliability of sensor data in situation awareness frameworks. In: Proceedings of IEEE international conference on systems, man, and cybernetics: cynertics, Hongkong, China, pp 2591–2596

  • Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15:4–31

    Article  Google Scholar 

  • Dasarathy BV (1997) Sensor fusion potential exploitation-innovative architectures and illustrative applications. Proc IEEE 85:24–38

    Article  Google Scholar 

  • Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceeding of the 6th international symposium on micro machine and human science, Nagoya, Japan, pp 39–43

  • El-taweel GS, Helmy AK (2013) Image fusion scheme based on modified dual pulse coupled neural network. Inform Process Lett 7:407–414

    Google Scholar 

  • Gaeta M, Lola V, Tomasiello S (2013) A generalized functional network for a classifier-quantifiers scheme in a gas-sensing system. Int J Intell Syst 28(10):988–1009

    Article  Google Scholar 

  • Gao S (2009) Multi-sensor optimal data fusion for INS/GPS/SAR integrated navigation system. Aerosp Sci Technol 13:232–237

    Article  Google Scholar 

  • Giampapa JA, Sycara K, Owens SR, Glinton R, Seo YW, Yu B, Grindle CE (2004) Extending the OneSAF Testbed into a C4ISR Testbed. Simulation 80:381–691

    Article  Google Scholar 

  • Judd JS (1990) Neural network design and the complexity of learning. MIT Press, Massachusetts

    Book  Google Scholar 

  • Kam M (1997) Sensor fusion for mobile robot navigation. Proc IEEE 85:108–119

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceeding of IEEE international conference on neural network, Perth, Australia, pp 1942–1948

  • Khaleghia B, Khamisa A, Karraya FO, Razavi SN (2013) Multisensor data fusion: a review of the state-of-the-art. Inform Fusion 14:28–44

    Article  Google Scholar 

  • Lambert DA (2006) A unification of sensor and higher-level fusion. In: Proceedings of the 9th international conference on information fusion, Florence, USA, pp 1–8

  • Leung Y, Ji NN, Ma JH (2013) An integrated information fusion approach based on the theory of evidence and group decision-making. Inform Fusion 14:410–422

    Article  Google Scholar 

  • Li X, Xu Q (2017) A reliable fusion positioning strategy for land vehicles in GPS-denied environments based on low-cost sensors. IEEE Trans Ind Electron 64(4):3205–3215

    Article  MathSciNet  Google Scholar 

  • Liggins ME, Hall DL, Linas J (2009) Handbook of multisensor data fusion: theory and practice, 2nd edn. Taylor & Francis Group, New York

    Google Scholar 

  • Maren AJ, Harston CT, Pap RM (2014) Handbook of neural computing applications. Academic Press, New York

    MATH  Google Scholar 

  • Mora B, Wulder MA, White JC (2013) An approach using Dempster–Shafer theory to fuse spatial data and satellite image derived crown metrics for estimation of forest stand leading species. Inform Fusion 14:384–395

    Article  Google Scholar 

  • Niu G, Han T, Yang BS, Tan ACC (2007) Multiagent decision fusion for motor fault diagnosis. Mechanical Syst Signal Process 21:1285–1299

    Article  Google Scholar 

  • Price K, Storn R, Lampinen J (2005) Differential evolution-a practical approach to global optimization. Springer, Berlin

    MATH  Google Scholar 

  • Salerno JJ (2007) Where’s level 2/3 fusion: a look back over the past 10 years. In: Proceedings of the 10th international conference on information fusion, Quebec, Canada, pp 1–4

  • Wang YM, Yang JB, Xu DL (2006) Environmental impact assessment using the evidential reasoning approach. Eur J Oper Res 174(3):1885–1913

    Article  MATH  Google Scholar 

  • Xu DL, Liu J, Yang JB (2007) Inference and learning methodology of belief-rule-based expert system for pipeline leak detection. Expert Syst Appl 32:103–113

    Article  Google Scholar 

  • Yadav AK, Chandel SS (2014) Solar radiation prediction using artificial neural network techniques: a review. Renew Sustain Energy Rev 33:772–781

    Article  Google Scholar 

  • Yang JB (2001) Rule and utility based evidential reasoning approach for multiple attribute decision analysis under uncertainty. Eur J Oper Res 131:31–61

    Article  MATH  Google Scholar 

  • Yang JB, Liu J, Xu DL, Wang J, Wang YM (2007) Optimization models for training belief-rule-based systems. IEEE Trans Syst Man Cybernet Part A Syst Hum 37:569–585

    Article  Google Scholar 

  • Yang JB, Wang YM, Xu DL (2012) Belief rule-based methodology for mapping consumer preferences and setting product targets. Expert Syst Appl 39:4749–4759

    Article  Google Scholar 

  • Zhu D (2008) Sensor fusion in integrated circuit fault diagnosis using a belief function model. Int J Distrib Sens Netw 4:247–261

    Article  Google Scholar 

Download references

Acknowledgements

This study was funded by NSFC under Grants 71601180 and 51665025, Applied Basic Research Foundation of Yunnan Province (No. 2015FB136).

Author information

Authors and Affiliations

  1. Material Management and UAV Engineering Academy, Air Force Engineering University, Xi’an, China

    Yu Zhou

  2. Department of Management, High-Tech Institute of Xi’an, Xi’an, China

    Leilei Chang

  3. Department of Automation, Kunming University of Science and Technology, Kunming, China

    Bin Qian

Authors
  1. Yu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leilei Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leilei Chang.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Human and animal rights

All applicable international, national and/or institutional guidelines for the care and use of animals were followed. This article does not contain any studies with human participants performed by any of the authors.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Y., Chang, L. & Qian, B. A belief-rule-based model for information fusion with insufficient multi-sensor data and domain knowledge using evolutionary algorithms with operator recommendations. Soft Comput 23, 5129–5142 (2019). https://doi.org/10.1007/s00500-018-3179-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3179-9

Keywords

Search

Navigation