Evolving Connectionist Systems for Adaptive Learning and Knowledge Discovery: Trends and Directions

Kasabov, N
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Journal Article
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This paper follows the 25 years of development of methods and systems for knowledge-based neural network systems and more specifically the recent evolving connectionist systems (ECOS). ECOS combine the adaptive/evolving learning ability of neural networks and the approximate reasoning and linguistically meaningful explanation features of symbolic representation, such as fuzzy rules. This review paper presents the classical now hybrid expert systems and evolving neuro-fuzzy systems, along with new developments in spiking neural networks, neurogenetic systems, and quantum inspired systems, all discussed from the point of few of their adaptability, model interpretability and knowledge discovery. The paper discusses new directions for the integration of principles from neural networks, fuzzy systems, bio- and neuroinformatics, and nature in general.

Knowledge-based systems , Neuro-fuzzy systems , Evolving Connectionist Systems , Evolving Spiking Neural Networks , Computational Neurogenetic Systems , Quantum inspired spiking neural networks , Spatio-temporal pattern recognition
Knowledge-Based Systems (2015), doi: http://dx.doi.org/10.1016/j.knosys. 2014.12.032
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Copyright © 2015 Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in (see Citation). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document.