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Models of computational intelligence in bioinformatics

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Computational Intelligence has emerged as a synergistic environment of Granular Computing (including fuzzy sets, rough sets, interval analysis), neural networks and evolutionary optimisation. This symbiotic framework addresses the needs of system modelling with regard to its transparency, accuracy and user friendliness. This becomes of paramount interest in various modelling in bioinformatics especially when we are concerned with decision-making processes. The objective of this study is to elaborate on the two essential features of CI that is Granular Computing and the resulting aspects of logic-oriented processing and its transparency. As the name stipulates, Granular Computing is concerned with processing carried out at a level of coherent conceptual entities - information granules. Such granules are viewed as inherently conceptual entities formed at some level of abstraction whose processing is rooted in the language of logic (especially, many valued or fuzzy logic). The logic facet of processing is cast in the realm of fuzzy logic and fuzzy sets that construct a consistent processing background necessary for operating on information granules. Several main categories of logic processing units (logic neurons) are discussed that support aggregative (and-like and or-like operators) and referential logic mechanisms (dominance, inclusion, and matching). We show how the logic neurons contribute to high functional transparency of granular processing, help capture prior domain knowledge and give rise to a diversity of the resulting models.
Rocznik
Tom
Strony
IP13--23
Opis fizyczny
Bibliogr. 25 poz., rys.
Twórcy
autor
  • Department of Electrical & Computer Engineeering, University of Alberta, Edmonton Canada T6R 2G7, Systems Research Institute, Polish Academy of Sciences, Warsaw Poland
Bibliografia
  • [1] A. BARGIELA, W. PEDRYCZ, Granular Computing: An Introduction, Kluwer Academic Publishers, Dordrecht, 2002.
  • [2] Z. BUBNICKI, Uncertain variables and their application to decision making problems, IEEE Trans. on Systems, Man, and Cybernetics –A, 31, no.6, 2001, 587-596.
  • [3] Z. BUBNICKI, Uncertain Logics, Variables and Systems, Springer Verlag, Berlin, 2002.
  • [4] J. CASILLAS et al. (eds.), Interpretability Issues in Fuzzy Modeling, Springer Verlag, Berlin, 2003.
  • [5] J. A. DICKERSON, M.S. LAN, Fuzzy rule extraction from numerical data for function approximation, IEEE Trans on System, Man, and Cybernetics -B, 26, 1995, 119-129.
  • [6] A.F. GOMEZ-SKARMETA, M. DELGADO, M.A. VILA, About the use of fuzzy clustering techniques for fuzzy model identification, Fuzzy Sets and Systems, 106, 1999, 179-188.
  • [7] E. GOLDBERG, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley,1989
  • [8] K. HIROTA, W. PEDRYCZ, OR/AND neuron in modeling fuzzy set connectives, IEEE Trans. on Fuzzy Systems, 2, 1994, 151-161.
  • [9] K. HIROTA, W. PEDRYCZ, Fuzzy relational compression, IEEE Trans. on Systems, Man, and Cybernetics-B, 29, 1999, 407-415.
  • [10] J. KACPRZYK, Wieloetapowe Sterowanie Rozmyte, PWN, Warszawa, 2001.
  • [11] B. KOSKO, Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, NJ, 1991.
  • [12] S. MITRA, S.K. PAL, Logical operation based fuzzy MLP for classification and rule generation, Neural Networks, 7, 1994, 353-373.
  • [13] S. MITRA, S.K. PAL, Fuzzy multiplayer perceptron, inferencing and rule generation, IEEE Trans. on Neural Networks, 6, 1995, 51-63.
  • [14] S.K. PAL, S. MITRA, Neuro-Fuzzy Pattern Recognition, J. Wiley, N. York, 1999.
  • [15] Z. PAWLAK, Rough Sets. Theoretical aspects of Reasoning about Data, Kluwer Academic Publishers, Dordercht, 1991.
  • [16] W. PEDRYCZ, Neurocomputations in relational systems, IEEE Trans. on Pattern Analysis and Machine Intelligence, 13, 1991, 289-297.
  • [17] W.PEDRYCZ, Fuzzy neural networks and neurocomputations, Fuzzy Sets and Systems, 56, 1993, 1-28.
  • [18] W. PEDRYCZ, A. ROCHA, Knowledge-based neural networks, IEEE Trans. on Fuzzy Systems,1, 1993, 254-266.
  • [19] W. PEDRYCZ, P. LAM, A.F. ROCHA, Distributed fuzzy modelling, IEEE Trans. on Systems, Man and Cybernetics-B, 5, 1995, 769 - 780.
  • [20] W. PEDRYCZ, F. GOMIDE, An Introduction to Fuzzy Sets: Analysis and Design, MIT Press, Boston, 1998.
  • [21] W. PEDRYCZ (ed.), Granular Computing. An Emerging Paradigm, Physica-Verlag, Heidelberg, 2001.
  • [22] M. SETNES, R. BABUSKA, H. VEBRUGGEN, Rule-based modeling: precision and transparency, IEEE Trans on System, Man, and Cybernetics - C, 28, 1998, 165-169.
  • [23] T. SUDKAMP, R.J. HAMMEL II, Rule base completion in fuzzy models, In: W. Pedrycz (ed.), Fuzzy Modelling: Paradigms and Practice, Kluwer Academic Publishers, Dordercht, 1996, pp. 313-330.
  • [24] L.A. ZADEH, Fuzzy logic-= computing with words, IEEE Trans. on Fuzzy Systems, 4, 1996, 103-111.
  • [25] L.A. ZADEH, Towards a theory of fuzzy information granulation and its application in human reasoning and fuzzy logic, Fuzzy Sets and Systems, 90, 1997, 111-127.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0019-0002
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