Abstract
The agenda of fuzzy neurocomputing focuses on the development of artifacts that are both adaptable (so any learning pursuits could be carried out in an efficient manner) and interpretable (so that the results are easily understood by the user or designer). The logic is the language of interpretable constructs. Neural architectures offer a flexible and convenient setting for learning. The study conveys a message that a suitable combination of logic incorporated into the structure of a specialized neuron leads to interpretable and elastic processing units one can refer to as fuzzy neurons. We investigate the main categories of such neurons and elaborate on the ensuing topologies of the networks emphasizing a remarkably rich landscape of logic architectures associated with the use of the logic neurons.
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References
Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Dordercht (2003)
De Baets, B.: Idempotent Uninorms. European Journal of Operational Research 118, 631–642 (1999)
De Baets, B., Fodor, F.: van Melle’s Combining Function in MYCIN is a Representable Uninorm: An Alternative Proof. Fuzzy Sets and Systems 104, 133–136 (1999)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, N. York (1981)
Calvo, T., De Baets, B., Fodor, J.: The Functional Equations of Frank and Alsina for Uninorms and Nullnorms. Fuzzy Sets and Systems 120, 385–394 (2001)
Calvo, T., Kolesárová, A., KomornÃková, M., Mesiar, R.: Aggregation Operators: Properties, Classes and Construction Methods. In: Aggregation Operators: New Trends and Applications, pp. 1–104. Physica-Verlag, Heildelberg (2002)
Calvo, T., Mesiar, R.: Continuous Generated Associative Aggregation Operators. Fuzzy Sets & Systems 126, 191–197 (2002)
Ciaramella, A., Tagliaferri, R., Pedrycz, W.: The Genetic Development of Ordinal Sums. Fuzzy Sets & Systems 151, 303–325 (2005)
Feng, Q.: Uninorm Solutions and Nullnorm Solutions to the Modularity Condition Equations. Fuzzy Sets & Systems 148, 231–242 (2004)
Fodor, J., Yager, R.R., Rybalov, A.: Structure of Uninorms. Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems 5, 113–126 (1997)
Fodor, J., Yager, R.R., Rybalov, A.: Structure of Uninorms. Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems 5, 411–427 (1997)
Hirota, K., Pedrycz, W.: OR/AND Neuron in Modeling Fuzzy Set Connectives. IEEE Trans. on Fuzzy Systems 2, 151–161 (1994)
Klement, E., Navara, M.: A Survey on Different Triangular Norm-Based fuzzy Logics. Fuzzy Sets and Systems 101, 241–251 (1999)
Klement, E., Mesiar, R., Pap, E.: Triangular Norms. Kluwer Academic Publishers, Dordrecht (2000)
Pedrycz, W.: Fuzzy Neural Networks and Neurocomputations. Fuzzy Sets & Systems 56, 1–28 (1993)
Pedrycz, W., Gomide, F.: Fuzzy Systems Engineering. J. Wiley, Hoboken (2007)
Pedrycz, W. (ed.): Granular Computing: An Emerging Paradigm. Physica Verlag, Heidelberg (2001)
Pedrycz, W.: Heterogeneous Fuzzy Logic Networks: Fundamentals and Development Studies. IEEE Trans. on Neural Networks 15, 1466–1481 (2004)
Pedrycz, W.: Knowledge-Based Clustering. J. Wiley & Sons, N. York (2005)
Schweizer, B., Sklar, A.: Probabilistic Metric Spaces. North-Holland, New York (1983)
Tasdiras, A., Mazrgaritas, K.: The MYCIN Certainty Factor Handling Function as Uninorm Operator and Its use as Threshold Function in Artificial Neurons. Fuzzy Sets and Systems 93, 263–274 (1998)
Yager, R.R.: On Weighted Median Aggregation. Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems 2, 101–114 (1994)
Yager, R.R., Rybalov, A.: Uninorm Aggregation Operators. Fuzzy Sets & Systems 80, 111–120 (1996)
Yager, R.R.: Uninorms in Fuzzy Systems Modeling. Fuzzy Sets & Systems 122, 167–175 (2001)
Yager, R.R.: Defending Against Strategic Manipulation in Uninorm-based Multi-agent Decision Making. European Journal of Operational Research 141, 217–232 (2002)
Zadeh, L.A.: Toward Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets & Systems 90, 111–127 (1997)
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Pedrycz, W. (2008). A Quest for Adaptable and Interpretable Architectures of Computational Intelligence. In: Jain, L.C., Sato-Ilic, M., Virvou, M., Tsihrintzis, G.A., Balas, V.E., Abeynayake, C. (eds) Computational Intelligence Paradigms. Studies in Computational Intelligence, vol 137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79474-5_2
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DOI: https://doi.org/10.1007/978-3-540-79474-5_2
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