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Interval mathematics for analysis of multi-level granularity

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EN
Abstrakty
EN
The more complex the problem, the more complex the system necessary for solving this problem. For very complex problems, it is no longer possible to design the corresponding system on a single resolution level, it becomes necessary to have multi-level, multiresolutional systems, with multi-level granulation. When analyzing such systems - e.g., when estimating their performance and/or their intelligence - it is reasonable to use the multi-level character of these systems: first, we analyze the system on the low-resolution level, and then we sharpen the results of the low-resolution analysis by considering higher-resolution representations of the analyzed system. The analysis of the low-resolution level provides us with an approximate value of the desired performance characteristic. In order to make a definite conclusion, we need to know the accuracy of this approximation. In this paper, we describe interval mathematics- a methodology for estimating such accuracy. The resulting interval approach is also extremely important for tessellating the space of search when searching for optimal control. We overview the corresponding theoretical results, and present several case studies.
Rocznik
Strony
323--350
Opis fizyczny
Bibliogr. 82 poz., tab., wzory
Twórcy
  • University of Texas, Computer Science, El Paso, TX 79968, USA
autor
  • University of Houston-Downtown, Computer Science and Math, Houston, TX 77002, USA
Bibliografia
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  • [37] M. Krishna: Using symmetries to reduce the size of a fuzzy knowledge base, with an application to fault detection. Master Thesis, Department of Computer Science, University of Texas at El Paso, 1999.
  • [38] M. Krishna, V. Kreinovich and R. Osegueda: Fuzzy Logic in Non-Destructive Testing of Aerospace Structures. In: Jaime Ramirez-Angulo (Ed.), Proc. the 1999 IEEE Midwest Symp. on Circuits and Systems, Las Cruces, New Mexico, 1 (1999), 431-434.
  • [39] C. Langrand, V. Kreinovich and H. T. Nguyen: Two-dimensional fuzzy logic for expert systems. Sixth Int. Fuzzy Systems Association World Congress, San Paulo, Brazil, 1 (1995), 221-224.
  • [40] J. Martinez, L. Macias, A. Esper, J. Chaparro, V. Alvarado, S. A. Starks and V. Kreinovich: Towards more realistic (e.g., non-associative) and-and or-operations in fuzzy logic. Proc. of the 2001 IEEE Systems, Man, and Cybernetics Conference, Tucson, Arizona, (2001), 2187-2192.
  • [41] N. J. McMillan: Temporal and spatial magmatic evolution of the Rio Grande rift. New Mexico Geological Survey Guidebook, 49th Field Conference, Las Cruces County, (1998), 107-116.
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  • [46] R. Moore: Methods and Applications of Interval Analysis. SIAM, Philadelphia, 1979.
  • [47] M. Mukaidono, Y. Yam and V. Kreinovich: Intervals is All We Need: An Argument. Proc. 8th Int. Fuzzy Systems Association World Congress, Taipei, Taiwan, (1999), 147-150.
  • [48] Hoang Phuong Nguyen, S. Starks and V. Kreinovich: Towards foundations for traditional oriental medicine’. In: Nguyen Hoang Phuong and A. Ohsato (Eds.), Proc. Vietnam-Japan Bilateral Symp. on Fuzzy Systems and Applications, Ha-Long Bay, Vietnam, (1998), 704-708.
  • [49] H. T. Nguyen and V. Kreinovich: Applications of continuous mathematics to computer science. Kluwer, Dordrecht, 1997.
  • [50] H. T. Nguyen, V. Kreinovich and Q. Zuo: Interval-valued degrees of belief: applications of interval computations to expert systems and intelligent control. Int. J. of Uncertainty, Fuzziness, and Knowledge-Based Systems, 5(3), (1997), 317-358.
  • [51] H. T. Nguyen, O. Kosheleva, V. Kreinovich and L. Ding: On the Optimal Choice of Quality Metric In Image Compression: A Soft Computing Approach. Proc. of the 6th Int. Conf. on Control, Automation, Robotics and Vision, Singapore, (2000).
  • [52] H. T. Nguyen and V. Kreinovich: Nested Intervals and Sets: Concepts, Relations to Fuzzy Sets, and Applications. In: R. B. Kearfott et. al. (Eds.), Applications of Interval Computations. Kluwer, Dordrecht, 1996, 245-290.
  • [53] H. T. Nguyen, V. Kreinovich and I. R. Goodman: Why Unary and Binary Operations in Logic: General Result Motivated by Interval-Valued Logics. Proc. J. 9th World Congress of the Int. Fuzzy Systems Association and 20th Int. Conf. of the North American Fuzzy Information Processing Society, Vancouver, Canada, (2001), 1991-1996.
  • [54] H. T. Nguyen, V. Kreinovich, G. N. Solopchenko and C.-W. Tao: Why Two Sigma? A Theoretical Justification. In: L. Reznik and V. Kreinovich (Eds.), Soft Computing in Measurements and Information Acquisition. Springer-Verlag, 2002 (to appear).
  • [55] H. T. Nguyen, V. Kreinovich and C.-W. Tao Why 95% and Two Sigma? A Theoretical Justification for an Empirical Measurement Practice. Proc. Int. Workshop on Intelligent Systems Resolutions: The 8th Bellman Continuum, Taipei, Taiwan, (2000), 358-362.
  • [56] H. T. Nguyen, V. Kreinovich and B. Wu: Fuzzy/probability_fractal/smooth. Int. J. of Uncertainty, Fuzziness, and Knowledge-Based Systems, 7(4), (1999), 363-370.
  • [57] H. T. Nguyen, N. R. Prasad, V. Kreinovich and H. Gassoumi: Some Practical Applications of Soft Computing and Data Mining. In: A. Kandel, H. Bunke, and M. Last (Eds.), Data Mining and Computational Intelligence. Springer-Verlag, Berlin, (2001), 273-307.
  • [58] H. T. Nguyen and E. A. Walker: First Course in Fuzzy Logic. CRC Press, Boca Raton, FL, 1999.
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  • [60] R. A. Osegueda, C. Ferregut, V. Kreinovich, S. Seetharami and H. Schulte: Fuzzy (Granular) Levels of Quality, With Applications to Data Mining and to Structural Integrity of Aerospace Structures. Proc. 19th Int. Conf. of the North American Fuzzy Information Society, Atlanta, Georgia, (2000), 348-352.
  • [61] R. A. Osegueda, S. R. Seelam, A. C. Holguin, V. Kreinovich and C.-W. Tao: Statistical and Dempster-Shafer Techniques in Testing Structural Integrity of Aerospace Structures. Proc. Int. Conf. on Intelligent Technologies, Bangkok, Thailand, (2000), 383-389.
  • [62] P. Santiprabhob, H. T. Nguyen, W. Pedrycz and V. Kreinovich: Logicmotivated choice of fuzzy logic operators. Proc. FUZZ-IEEE’2001, Melbourne, Australia, (2001).
  • [63] M. Siddaiah, M. A. Lieberman, N. R. Prasad and V. Kreinovich: A geometric approach to classification of trash in ginned cotton. Geombinatorics, 10(2), (2000), 77-82.
  • [64] S. Srikrishnan, R. Araiza, H. Xie, S. A. Starks and V. Kreinovich: Automatic referencing of satellite and radar images. Proc. IEEE Systems, Man, and Cybernetics Conference, Tucson, Arizona, (2001), 2176-2181.
  • [65] S. A. Starks and V. Kreinovich: Multi-spectral inverse problems in satellite image processing. In: A. Mohamad-Djafari (Ed.), Bayesian Inference for Inverse Problems. Proc. SPIE/Int. Society for Optical Engineering, 3459, San Diego, CA, (1998), 138-146.
  • [66] S. A. Starks and V. Kreinovich: Locating the whole pattern is better than locating its pieces: a geometric explanation of an empirical phenomenon. Geombinatorics, 8(4), (1999), 116-121.
  • [67] S. A. Starks and V. Kreinovich: Aerospace applications of soft computing and interval computations (with an emphasis on multi-spectral satellite imaging). In: M. Jamshidi, M. Fathi, and T. Furunashi (Eds.), Soft Computing, Multimedia, and Image Processing. Proc. World Automation Congress, Maui, Hawaii, (2000), 644-651.
  • [68] S. Subbaramu, A. Q. Gates and V. Kreinovich: Application of Kolmogorov complexity to image processing: it is possible to have a better compression, but it is not possible to have the best one. Bulletin of the European Association for Theoretical Computer Science, 69 (1999), 145-150.
  • [69] R. Trejo and Vladik Kreinovich: Error Estimations for Indirect Measurements: Randomized vs. Deterministic Algorithms For ‘Black-Box’ Programs. S. Rajasekaran, P. Pardalos, J. Reif, and J. Rolim (Eds.), Handbook on Randomized Computing. Kluwer, 2001.
  • [70] R. Trejo, V. Kreinovich, I. R. Goodman, J.Martinez And R. Gonzalez: A Realistic (Non-Associative) Logic And a Possible Explanations of 7+-2 Law. Int. J. of Approximate Reasoning, 29 (2002) 235-266.
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  • [78] website on interval computations http://www.cs.utep.edu/interval-comp
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Bibliografia
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