Download e-book for kindle: Advances in computational intelligence: theory & by Derong Liu, Fei-Yue Wang

By Derong Liu, Fei-Yue Wang

ISBN-10: 9812567348

ISBN-13: 9789812567345

ISBN-10: 9812773924

ISBN-13: 9789812773920

Computational Intelligence (CI) is a lately rising region in primary and utilized learn, exploiting a few complex details processing applied sciences that generally embrace neural networks, fuzzy good judgment and evolutionary computation. With a big problem to exploiting the tolerance for imperfection, uncertainty, and partial fact to accomplish tractability, robustness and occasional resolution expense, it turns into glaring that composing tools of CI will be operating simultaneously instead of individually. it truly is this conviction that examine at the synergism of CI paradigms has skilled major development within the final decade with a few parts nearing adulthood whereas many others closing unresolved. This publication systematically summarizes the most recent findings and sheds gentle at the respective fields that will result in destiny breakthroughs.

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165-169,1998. [23] T. Sudkamp and R. J. Hammel II, "Rule base completion in fuzzy models," In Fuzzy Modelling: Paradigms and Practice, W. ), Dordercht: Kluwer Academic Publishers, 1996, pp. 313-330. [24] L. A. Zadeh, "Fuzzy logic = computing with words," IEEE Trans, on Fuzzy Systems, vol. 4, pp. 103-111, 1996. [25] L. A. Zadeh, "Towards a theory of fuzzy information granulation and its application in human reasoning and fuzzy logic," Fuzzy Sets and Systems, vol. 90, pp. 111-127,1997. 21 This page is intentionally left blank Chapter 2 Abstraction and Linguistic Analysis of Conventional Numerical Dynamic Systems Fei-Yue Wang Abstract: Linguistic dynamic systems (LDS) are dynamic processes involving mainly computing with words instead of numbers for modeling and analysis of complex systems and human-machine interfaces.

Bubnicki, "Uncertain variables and their application to decision making problems," IEEE Trans, on Systems, Man, and Cybernetics-A, vol. 31, no. 6, pp. 587-596,2001. [3] Z. Bubnicki, Uncertain Logics, Variables and Systems, Berlin: Springer Verlag, 2002. [4] J. Casillas et al. ), Interpretability Issues in Fuzzy Modeling, Berlin: Springer Verlag, 2003. [5] J. A. Dickerson and M. S. Lan, "Fuzzy rule extraction from numerical data for function approximation," IEEE Trans on System, Man, and CyberneticsB, vol.

The naming of the neurons reflect the underlying processing realized by them. The aggregative neurons concentrate on the logic type of aggregation of the inputs (truth values) while the referential neurons are aimed at logic processing of results of referential transformations of the corresponding truth values. 7 8 W. Pedrycz Aggregative neurons Formally, these neurons realize a logic mapping from [0,1]" to [0,1]. Two main classes of the processing units exist in this category. (i) OR neuron realizes an and logic aggregation of inputs x = [x\,X2,--- , xn] with the corresponding connections (weights) w = [K>I,W2, • • • ,wn] and then summarizes the partial results in an or-wise manner (hence the name of the neuron).

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Advances in computational intelligence: theory & applications by Derong Liu, Fei-Yue Wang

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