Brief Introduction to Computational Intelligence

Angelov, P.P. and Gu, X. (2019) Brief Introduction to Computational Intelligence. In: Empirical Approach to Machine Learning. Studies in Computational Intelligence, 800 . Springer-Verlag, pp. 69-99. ISBN 9783030023836

Full text not available from this repository.

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.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
129599
Deposited By:
Deposited On:
08 Jan 2019 15:15
Refereed?:
Yes
Published?:
Published
Last Modified:
30 Mar 2020 10:00