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Brief Introduction to Computational Intelligence

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

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Brief Introduction to Computational Intelligence. / Angelov, P.P.; Gu, X.
Empirical Approach to Machine Learning. ed. / Plamen Angelov; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. p. 69-99 (Studies in Computational Intelligence; Vol. 800).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

Harvard

Angelov, PP & Gu, X 2019, Brief Introduction to Computational Intelligence. in P Angelov & X Gu (eds), Empirical Approach to Machine Learning. vol. 800, Studies in Computational Intelligence, vol. 800, Springer-Verlag, pp. 69-99. https://doi.org/10.1007/978-3-030-02384-3_3

APA

Angelov, P. P., & Gu, X. (2019). Brief Introduction to Computational Intelligence. In P. Angelov, & X. Gu (Eds.), Empirical Approach to Machine Learning (Vol. 800, pp. 69-99). (Studies in Computational Intelligence; Vol. 800). Springer-Verlag. https://doi.org/10.1007/978-3-030-02384-3_3

Vancouver

Angelov PP, Gu X. Brief Introduction to Computational Intelligence. In Angelov P, Gu X, editors, Empirical Approach to Machine Learning. Vol. 800. Springer-Verlag. 2019. p. 69-99. (Studies in Computational Intelligence). Epub 2018 Oct 18. doi: 10.1007/978-3-030-02384-3_3

Author

Angelov, P.P. ; Gu, X. / Brief Introduction to Computational Intelligence. Empirical Approach to Machine Learning. editor / Plamen Angelov ; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. pp. 69-99 (Studies in Computational Intelligence).

Bibtex

@inbook{6e7b48e5d2a1469683b5b0602971913b,
title = "Brief Introduction to Computational Intelligence",
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. {\textcopyright} 2019, Springer Nature Switzerland AG.",
author = "P.P. Angelov and X. Gu",
year = "2019",
doi = "10.1007/978-3-030-02384-3_3",
language = "English",
isbn = "9783030023836",
volume = "800",
series = "Studies in Computational Intelligence",
publisher = "Springer-Verlag",
pages = "69--99",
editor = "Angelov, {Plamen } and Xiaowei Gu",
booktitle = "Empirical Approach to Machine Learning",

}

RIS

TY - CHAP

T1 - Brief Introduction to Computational Intelligence

AU - Angelov, P.P.

AU - Gu, X.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-030-02384-3_3

DO - 10.1007/978-3-030-02384-3_3

M3 - Chapter (peer-reviewed)

SN - 9783030023836

VL - 800

T3 - Studies in Computational Intelligence

SP - 69

EP - 99

BT - Empirical Approach to Machine Learning

A2 - Angelov, Plamen

A2 - Gu, Xiaowei

PB - Springer-Verlag

ER -