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

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.