Download Advances in Computational Intelligence: Theory And by Derong Liu, Fei-Yue Wang PDF

By Derong Liu, Fei-Yue Wang

Computational Intelligence (CI) is a lately rising quarter in basic and utilized learn, exploiting a couple of complex info processing applied sciences that quite often include neural networks, fuzzy good judgment and evolutionary computation. With an incredible difficulty to exploiting the tolerance for imperfection, uncertainty, and partial fact to accomplish tractability, robustness and occasional answer expense, it turns into obvious that composing equipment of CI may be operating simultaneously instead of individually. it's this conviction that learn at the synergism of CI paradigms has skilled major progress within the final decade with a few parts nearing adulthood whereas many others ultimate unresolved. This publication systematically summarizes the most recent findings and sheds mild at the respective fields that would result in destiny breakthroughs.

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Pedrycz and F. Gomide, An Introduction to Fuzzy Sets: Analysis and Design, Boston: MIT Press, 1998. [21] W. ), Granular Computing. An Emerging Paradigm, Heidelberg: Physica-Verlag, 2001. [22] M. Setnes, R. Babuska, and H. Vebruggen, "Rule-based modeling: Precision and transparency," IEEE Trans on System, Man, and Cybernetics-C, vol. 28, pp. 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.

Being also motivated by the genuine need of constructing networks that exhibit plasticity while retaining interpretability, we have developed a heterogeneous structure composed of logic neurons. The two main categories of aggregative and reference neurons are deeply rooted in the fundamental operations encountered in fuzzy sets (including logic operations, linguistic modifiers, and logic reference operations). The direct interpretability of the network we addressed in the study helps develop a transparent logic description of data.

In [22-26], Wang have used Kosko's interpretation [10] of fuzzy sets to consider LDS as mappings on fuzzy hypercubes; and by introducing cellular structures on hypercubes using equi-distribution lattices developed in number theory [8], these mappings can be approximated as cell-to-cell mappings in a cellular space [6, 7], in which each cell represents a linguistic term (a word) defined by a family of membership functions of fuzzy sets; in this way, LDS can be studied in the cellular space, and thus, methods and concepts of analysis and synthesis developed for conventional nonlinear systems, such as stability analysis and design synthesis, can be modified and applied for LDS; while cell-to-cell mappings provide us with a very general numeric tool for studying LDS, it is not the most effective method to handle the special cases of type-I and type-II LDS to be studied here.

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