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Autonomous Learning Multi-model Systems

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

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Autonomous Learning Multi-model Systems. / Angelov, P.P.; Gu, X.

Empirical Approach to Machine Learning. ed. / Plamen Angelov; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. p. 199-222 (Studies in Computational Intelligence; Vol. 800).

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

Harvard

Angelov, PP & Gu, X 2019, Autonomous Learning Multi-model Systems. in P Angelov & X Gu (eds), Empirical Approach to Machine Learning. vol. 800, Studies in Computational Intelligence, vol. 800, Springer-Verlag, pp. 199-222. https://doi.org/10.1007/978-3-030-02384-3_8

APA

Angelov, P. P., & Gu, X. (2019). Autonomous Learning Multi-model Systems. In P. Angelov, & X. Gu (Eds.), Empirical Approach to Machine Learning (Vol. 800, pp. 199-222). (Studies in Computational Intelligence; Vol. 800). Springer-Verlag. https://doi.org/10.1007/978-3-030-02384-3_8

Vancouver

Angelov PP, Gu X. Autonomous Learning Multi-model Systems. In Angelov P, Gu X, editors, Empirical Approach to Machine Learning. Vol. 800. Springer-Verlag. 2019. p. 199-222. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-030-02384-3_8

Author

Angelov, P.P. ; Gu, X. / Autonomous Learning Multi-model Systems. Empirical Approach to Machine Learning. editor / Plamen Angelov ; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. pp. 199-222 (Studies in Computational Intelligence).

Bibtex

@inbook{372c225b943842d48878de81b7138fd6,
title = "Autonomous Learning Multi-model Systems",
abstract = "In this chapter, the Autonomous Learning Multi-Model (ALMMo) systems are introduced, which are based on the AnYa type neuro-fuzzy systems and can be seen as an universal self-developing, self-evolving, stable, locally optimal proven universal approximators. This chapter starts with the general concepts and principles of the zero- and first-order ALMMo systems, and, then, describes the architecture followed by the learning methods. The ALMMo system does not impose generation models with parameters on the empirically observed data, and has the advantages of being non-parametric, non-iterative and assumption-free, and, thus, it can objectively disclose the underlying data pattern. With a prototype-based nature, the ALMMo system is able to self-develop, self-learn and evolve autonomously. The theoretical proof (using Lyapunov theorem) of the stability of the first-order ALMMo systems is provided demonstrating that the first-order ALMMo systems are also stable. The theoretical proof of the local optimality which satisfies Karush-Kuhn-Tucker conditions is also given. {\textcopyright} 2019, Springer Nature Switzerland AG.",
author = "P.P. Angelov and X. Gu",
year = "2019",
doi = "10.1007/978-3-030-02384-3_8",
language = "English",
isbn = "9783030023836",
volume = "800",
series = "Studies in Computational Intelligence",
publisher = "Springer-Verlag",
pages = "199--222",
editor = "Angelov, {Plamen } and Gu, {Xiaowei }",
booktitle = "Empirical Approach to Machine Learning",

}

RIS

TY - CHAP

T1 - Autonomous Learning Multi-model Systems

AU - Angelov, P.P.

AU - Gu, X.

PY - 2019

Y1 - 2019

N2 - In this chapter, the Autonomous Learning Multi-Model (ALMMo) systems are introduced, which are based on the AnYa type neuro-fuzzy systems and can be seen as an universal self-developing, self-evolving, stable, locally optimal proven universal approximators. This chapter starts with the general concepts and principles of the zero- and first-order ALMMo systems, and, then, describes the architecture followed by the learning methods. The ALMMo system does not impose generation models with parameters on the empirically observed data, and has the advantages of being non-parametric, non-iterative and assumption-free, and, thus, it can objectively disclose the underlying data pattern. With a prototype-based nature, the ALMMo system is able to self-develop, self-learn and evolve autonomously. The theoretical proof (using Lyapunov theorem) of the stability of the first-order ALMMo systems is provided demonstrating that the first-order ALMMo systems are also stable. The theoretical proof of the local optimality which satisfies Karush-Kuhn-Tucker conditions is also given. © 2019, Springer Nature Switzerland AG.

AB - In this chapter, the Autonomous Learning Multi-Model (ALMMo) systems are introduced, which are based on the AnYa type neuro-fuzzy systems and can be seen as an universal self-developing, self-evolving, stable, locally optimal proven universal approximators. This chapter starts with the general concepts and principles of the zero- and first-order ALMMo systems, and, then, describes the architecture followed by the learning methods. The ALMMo system does not impose generation models with parameters on the empirically observed data, and has the advantages of being non-parametric, non-iterative and assumption-free, and, thus, it can objectively disclose the underlying data pattern. With a prototype-based nature, the ALMMo system is able to self-develop, self-learn and evolve autonomously. The theoretical proof (using Lyapunov theorem) of the stability of the first-order ALMMo systems is provided demonstrating that the first-order ALMMo systems are also stable. The theoretical proof of the local optimality which satisfies Karush-Kuhn-Tucker conditions is also given. © 2019, Springer Nature Switzerland AG.

U2 - 10.1007/978-3-030-02384-3_8

DO - 10.1007/978-3-030-02384-3_8

M3 - Chapter (peer-reviewed)

SN - 9783030023836

VL - 800

T3 - Studies in Computational Intelligence

SP - 199

EP - 222

BT - Empirical Approach to Machine Learning

A2 - Angelov, Plamen

A2 - Gu, Xiaowei

PB - Springer-Verlag

ER -