Hard and Soft Adjusting of a Parameter With Its Known Boundaries by the Value Based on the Experts’ Estimations Limited to the Parameter
DOI:
https://doi.org/10.1515/ecce-2016-0003Keywords:
Expert procedure, Experts’ estimations, Law of large numbers, Parameter adjustment, ToleranceAbstract
Adjustment of an unknown parameter of the multistage expert procedure is considered. The lower and upper boundaries of the parameter are counted to be known. A key condition showing that experts’ estimations are satisfactory in the current procedure is an inequality, in which the value based on the estimations is not greater than the parameter. The algorithms of hard and soft adjusting are developed. If the inequality is true and its both terms are too close for a long sequence of expert procedures, the adjusting can be early stopped. The algorithms are reversible, implying inversion to the reverse inequality and sliding up off the lower boundary.References
T. Frantti and M. Majanen, “An expert system for real-time traffic management in wireless local area networks,” Expert Systems with Applicat., vol. 41, issue 10, pp. 4996–5008, Aug. 2014. https://doi.org/10.1016/j.eswa.2014.01.024
J. Protasiewicz, W. Pedrycz, M. Kozłowski, S. Dadas, T. Stanisławek, A. Kopacz and M. Gałężewska, “A recommender system of reviewers and experts in reviewing problems,” Knowledge-Based Systems, vol. 106, pp. 164–178, 2016. https://doi.org/10.1016/j.knosys.2016.05.041
Y. Zhou, N. Fenton and M. Neil, “Bayesian network approach to multinomial parameter learning using data and expert judgments,” Int. J. of Approximate Reasoning, vol. 55, issue 5, pp. 1252–1268, July 2014. https://doi.org/10.1016/j.ijar.2014.02.008
G. Vessia, L. Pisano, C. Vennari, M. Rossi and M. Parise, “Mimic expert judgement through automated procedure for selecting rainfall events responsible for shallow landslide: A statistical approach to validation,” Computers & Geosciences, vol. 86, pp. 146–153, Jan. 2016. https://doi.org/10.1016/j.cageo.2015.10.015
M. J. Kochenderfer, C. Amato, G. Chowdhary, J. P. How, H. J. Davison Reynolds, J. R. Thornton, P. A. Torres-Carrasquillo, N. K. Üre and J. Vian, Decision Making Under Uncertainty: Theory and Application. Cambridge, Massachusetts, London, England: The MIT Press, 2015.
T. A. Saurin and S. S. Gonzalez, “Assessing the compatibility of the management of standardized procedures with the complexity of a sociotechnical system: Case study of a control room in an oil refinery,” Applied Ergonom., vol. 44, issue 5, pp. 811–823, Sep. 2013. https://doi.org/10.1016/j.apergo.2013.02.003
R. E. Walpole, R. H. Myers, S. L. Myers and K. Ye, Probability & Statistics for Engineers & Scientists, 9th ed. Boston, Massachusetts: Prentice Hall, 2012.
Z. Xuan, H. Xia, and Y. Du, “Adjustment of knowledge-connection structure affects the performance of knowledge transfer,” Expert Systems with Applicat., vol. 38, issue 12, pp. 14935–14944, Nov.–Dec. 2011. https://doi.org/10.1016/j.eswa.2011.05.054
Y.-M. Wang, L.-H. Yang, Y.-G. Fu, L.-L. Chang and K.-S. Chin, “Dynamic rule adjustment approach for optimizing belief rule-base expert system,” Knowledge-Based Systems, vol. 96, pp. 40–60, Mar. 2016. https://doi.org/10.1016/j.knosys.2016.01.003
L. Barreira and C. Valls, “Robust nonuniform dichotomies and parameter dependence,” J. of Math. Analysis and Applicat., vol. 373, issue 2, pp. 690–708, Jan. 2011. https://doi.org/10.1016/j.jmaa.2010.08.026
D. Stoica, “Uniform exponential dichotomy of stochastic cocycles,” Stochastic Processes and their Applicat., vol. 120, issue 10, pp. 1920–1928, Sep. 2010. https://doi.org/10.1016/j.spa.2010.05.016
P. Ramazi, H. Hjalmarsson and J. Mårtensson, “Variance analysis of identified linear MISO models having spatially correlated inputs, with application to parallel Hammerstein models,” Automatica, vol. 50, issue 6, 2014, pp. 1675–1683. https://doi.org/10.1016/j.automatica.2014.04.014
O. J. Walch and M. C. Eisenberg, “Parameter identifiability and identifiable combinations in generalized Hodgkin–Huxley models,” Neurocomputing, vol. 199, pp. 137–143, July 2016. https://doi.org/10.1016/j.neucom.2016.03.027
A. Dembińska and N. Balakrishnan, “The asymptotic distribution of numbers of observations near order statistics,” J. of Statistical Planning and Inference, vol. 138, issue 8, pp. 2552–2562, Aug. 2008. https://doi.org/10.1016/j.jspi.2007.10.023
S. You, L. Hu, W. Mao, and X. Mao, “Robustly exponential stabilization of hybrid uncertain systems by feedback controls based on discrete-time observations,” Statistics & Probability Lett., vol. 102, pp. 8–16, July 2015. https://doi.org/10.1016/j.spl.2015.03.006
Y. Song, X. Wang, L. Lei, and S. Yue, “Uncertainty measure for interval-valued belief structures,” Measurement, vol. 80, pp. 241–250, Feb. 2016. https://doi.org/10.1016/j.measurement.2015.11.032
J. Wu, J. Gao, Z. Luo, and T. Brown, “Robust topology optimization for structures under interval uncertainty,” Advances in Eng. Software, vol. 99, pp. 36–48, Sep. 2016. https://doi.org/10.1016/j.advengsoft.2016.05.002
P. Révész, Z. W. Birnbaum, and E. Lukacs, The Laws of Large Numbers. New York, New York, London, England: Academic Press, 1968.
Downloads
Published
Issue
Section
License
Copyright (c) 2016 Vadim V. Romanuke (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.