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Brief Introduction to Computational Intelligence

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)

Published
Publication date2019
Host publicationEmpirical Approach to Machine Learning
EditorsPlamen Angelov, Xiaowei Gu
PublisherSpringer-Verlag
Pages69-99
Number of pages31
Volume800
ISBN (Print)9783030023836
Original languageEnglish

Publication series

NameStudies in Computational Intelligence
PublisherSpringer
Volume800
ISSN (Print)1860-949X

Abstract

This chapter provides a detailed introduction to the basic concepts and the general principles of the fuzzy sets and systems theory. Three major types of FRB systems are also covered and their differences are analyzed. The design of FRB systems is also covered. This chapter further moves on to the ANNs, which include the feedforward neural networks and three types of deep learning models. Both of the FRB systems and the ANNs have been proven universal approximators and can be designed based on the data. FRB systems have transparent, human-interpretable internal representation and can take advantage of the human domain expert knowledge. They are excellent in dealing with uncertainties, and they can self-organize, self-update both the structures and parameters in an online, dynamic environment. While ANNs are excellent in providing high precisions in most cases, they are fragile when facing new data patterns. They are typical examples of “black box” systems, their training process is usually limited to offline mode and requires huge amount of computation resources and data. © 2019, Springer Nature Switzerland AG.