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Autonomous Learning Multi-model Systems

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
Pages199-222
Number of pages24
Volume800
ISBN (Print)9783030023836
Original languageEnglish

Publication series

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

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. © 2019, Springer Nature Switzerland AG.