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Empirical Approach to Machine Learning pp 199–222Cite as

Autonomous Learning Multi-model Systems

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 800))

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.

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Authors and Affiliations

  1. School of Computing and Communications, Lancaster University, Lancaster, UK

    Plamen P. Angelov & Xiaowei Gu

Authors
  1. Plamen P. Angelov
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  2. Xiaowei Gu
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Correspondence to Plamen P. Angelov .

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

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