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An introduction to brain emotional learning inspired models (BELiMs) with an example of BELiMs’ applications

An introduction to brain emotional learning inspired models (BELiMs) with an example of BELiMs’... Brain emotional learning-inspired models (BELiMs) is a new category of computational intelligence (CI) paradigms. The general structure of BELiMs is based on the neural structure of the emotion system which processes and evaluates fear-induced stimuli, to produce emotional responses. The function of a BELiM is implemented by assigning adaptive networks to different parts of its structure. The primary motivation for developing BELiMs is to address model and time complexity issues associated with supervised machine learning artificial neural networks and neuro-fuzzy methods. One of the applications of BELiMs is chaotic time series prediction problems. A BEliM can be used as a time series prediction model. This paper introduces BELiMs as a new CI paradigm and explains historical, theoretical, structural and functional aspects of BELiMs. I also validate and evaluate the performance of BELiMs as a time series prediction model by examining different variations of BELiMs on benchmark time series data sets and comparing obtained results with different CI models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

An introduction to brain emotional learning inspired models (BELiMs) with an example of BELiMs’ applications

Parsapoor, Mahboobeh
Artificial Intelligence Review , Volume 52 (1) – Sep 4, 2018
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References (31)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Nature B.V.
Subject
Computer Science; Artificial Intelligence; Computer Science, general
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-018-9638-y
Publisher site
See Article on Publisher Site

Abstract

Brain emotional learning-inspired models (BELiMs) is a new category of computational intelligence (CI) paradigms. The general structure of BELiMs is based on the neural structure of the emotion system which processes and evaluates fear-induced stimuli, to produce emotional responses. The function of a BELiM is implemented by assigning adaptive networks to different parts of its structure. The primary motivation for developing BELiMs is to address model and time complexity issues associated with supervised machine learning artificial neural networks and neuro-fuzzy methods. One of the applications of BELiMs is chaotic time series prediction problems. A BEliM can be used as a time series prediction model. This paper introduces BELiMs as a new CI paradigm and explains historical, theoretical, structural and functional aspects of BELiMs. I also validate and evaluate the performance of BELiMs as a time series prediction model by examining different variations of BELiMs on benchmark time series data sets and comparing obtained results with different CI models.

Journal

Artificial Intelligence Review – Springer Journals

Published: Sep 4, 2018

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