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Dynamic behavioral assessment model based on Hebb learning rule

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Abstract

Behavioral assessment based on computing system is with important value for computer-simulated training and system diagnosis. However, the existing assessment is a static method for ex post evaluation, and the low efficiency and high complexity have been the urgent problems to be solved in the academic field. In this paper, we propose an adaptive dynamic behavioral assessment model based on Hebb learning rule that effectively combines the assessment standard and the weights of factors. The dynamic behavioral assessment considers the relative weights between the assessment indexes, whereas the existing assessment method does not; the dynamic behavioral assessment uses the assessment standard data recursively and can conduct an instant assessment for the objectives. We have built an assessment system for computer-simulated training, and took the pilot behavioral assessment for example to test and verify the dynamic behavioral assessment mode. Experimental results show that the dynamic behavioral assessment model based on Hebb learning rule has more advantage in assessment efficiency and online computing support.

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References

  1. Xenias D, Axon CJ, Whitmarsh L (2015) UK smart grid development: an expert assessment of the benefits, pitfalls and functions. Renew Energy 81:89–102

    Article  Google Scholar 

  2. Teferra K, Shields MD, Hapij A (2014) Mapping model validation metrics to subject matter expert scores for model adequacy assessment. Reliab Eng Syst Saf 132:9–19

    Article  Google Scholar 

  3. Nishijima T, Llorens-Martin M, Sanchez Tejeda G et al (2013) Cessation of voluntary wheel running increases anxiety-like behavior and impairs adult hippocampal neurogenesis in mice. Behav Brain Res 245:34–41

    Article  Google Scholar 

  4. Stein DS, Munir KM, Karweck AJ (2013) Developmental regression, depression, and psychosocial stress in an adolescent with Down syndrome. J Dev Behav Pediatr 34(3):216–218

    Article  Google Scholar 

  5. Kaplanski G, Levy H (2015) Value-at-risk capital requirement regulation, risk taking and asset allocation: a mean–variance analysis. Eur J Finance 21(3):215–241

    Article  Google Scholar 

  6. Chen B, Huang SF, Pan G (2015) High dimensional mean–variance optimization through factor analysis. J Multivar Anal 133:140–159

    Article  MathSciNet  MATH  Google Scholar 

  7. Zhang SC, Chen F, Wu XD, Zhang CQ, Wang RL (2012) Mining bridging rules between conceptual clusters. Appl Intell 36(1):108–118

    Article  Google Scholar 

  8. Zhang SC (2012) Decision tree classifiers sensitive to heterogeneous costs. J Syst Softw 85(4):771–779

    Article  Google Scholar 

  9. Wang XJ, Durugbo C (2013) Analysing network uncertainty for industrial product-service delivery: a hybrid fuzzy approach. Expert Syst Appl 40(11):4621–4636

    Article  Google Scholar 

  10. Duque-Ramos A, Tomas Fernandez-Breis J, Iniesta M et al (2013) Evaluation of the OQuaRE framework for ontology quality. Expert Syst Appl 40(7):2696–2703

    Article  Google Scholar 

  11. Tansel IN, Demetgul M, Bickraj K et al (2013) Basic computational tools and mechanical hardware for torque-based diagnostic of machining operations. J Intell Manuf 24:147–161

    Article  Google Scholar 

  12. Lai ZF, Jiang YF (2007) Learning action model in AI planning based on genetic algorithm. Chin J Comput 30(6):945–953

    MathSciNet  Google Scholar 

  13. Arroyo JM, Fernandez FJ (2013) Application of a genetic algorithm to n − K power system security assessment. Int J Electr Power Energy Syst 49(1):114–121

    Article  Google Scholar 

  14. Luque-Baena RM, Elizondo D, Lopez-Rubio E, Palomo EJ, Watson T (2013) Assessment of geometric features for individual identification and verification in biometric hand systems. Expert Syst Appl 40(9):3580–3594

    Article  Google Scholar 

  15. Wang T, Qin ZX, Zhang SC, Zhang CQ (2012) Cost-sensitive classification with inadequate labeled data. Inf Syst 37:508–516

    Article  Google Scholar 

  16. Knight C, Lindberg GE, Voth GA (2012) Multiscale reactive molecular dynamics. J Chem Phys 137:1–15

    Article  Google Scholar 

  17. Huang H, Xiao JC, Yang QS (2013) Creating process-agents incrementally by mining process asset library. Inf Sci 233(1):183–199

    Article  Google Scholar 

  18. Zhou L, Chen XP, Huang G, Sun YC, Mei H (2008) Negotiation-enabled modeling and verification of architectural behavior of internetware. Chin J Softw 15(5):1099–1112

    Article  Google Scholar 

  19. Hebb D (1949) Organization of behavior. Wiley, New York

    Google Scholar 

  20. Yin Y, Gong G, Han L (2011) Theory and techniques of data mining in cgf behavior modeling. Sci China Inf Sci 54(4):717–731

    Article  Google Scholar 

  21. Yin Y, Gong G, Han L (2009) Control approach to rough set reduction. Comput Math Appl 57(1):117–126

    Article  MATH  Google Scholar 

  22. Gao WQ, Zhang FC, Wang LB, Liu H (2011) Flight training performance assessment model and implementation. Electr Des Eng 19(24):50–52

    Google Scholar 

  23. Nunes TM, de Albuquerque VHC, Papa JP (2013) Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals. Expert Syst Appl 40(8):3096–3105

    Article  Google Scholar 

Download references

Acknowledgments

The research was supported by Fundamental Research Funds for the Central Universities (Project No. CDJZR14185501), Basic Research on the Frontier and Application of Chongqing City under Grant cstc2015jcyjA40006 and the Jiangsu Key Laboratory of Meteorological Observation and Information Processing (Project No. KDXS1502).

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Authors and Affiliations

  1. Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, Chongqing, China

    Yunfei Yin

  2. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Yunfei Yin, Hailong Yuan & Beilei Zhang

  3. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing, China

    Yunfei Yin

Authors
  1. Yunfei Yin
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  2. Hailong Yuan
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  3. Beilei Zhang
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Correspondence to Yunfei Yin.

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Yin, Y., Yuan, H. & Zhang, B. Dynamic behavioral assessment model based on Hebb learning rule. Neural Comput & Applic 28 (Suppl 1), 245–257 (2017). https://doi.org/10.1007/s00521-016-2341-5

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  • DOI: https://doi.org/10.1007/s00521-016-2341-5

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