Learning based brain emotional intelligence as a new aspect for development of an alarm system

Abstract

The multi criteria and purposeful prediction approach has been introduced and is implemented by the fast and efficient behavioral based brain emotional learning method. On the other side, the emotional learning from brain model has shown good performance and is characterized by high generalization property. New approach is developed to deal with low computational and memory resources and can be used with the largest available data sets. The scope of paper is to reveal the advantages of emotional learning interpretations of brain as a purposeful forecasting system designed to warning; and to make a fair comparison between the successful neural (MLP) and neurofuzzy (ANFIS) approaches in their best structures and according to prediction accuracy, generalization, and computational complexity. The auroral electrojet (AE) index are used as practical examples of chaotic time series and introduced method used to make predictions and warning of geomagnetic disturbances and geomagnetic storms based on AE index.

Improving the predictive performance of SAFEL: A Situation-Aware FEar Learning model

“…One of the most influential works in artificial fear conditioning is the brain emotional learning (BEL) model, proposed by Morén and Balkenius [6] and widely used in a variety of engineering and industrial applications [7], [8], [9], [10]. Their model consists of interconnected modules of artificial neural networks (ANNs) that simulate the role of neural circuitries involved in fear learning.…”

Modeling the thermal behavior of fluid flow inside channels using an artificial locally linear neuro-fuzzy approach

“…By now, intelligent approaches have addressed both fully-rational features [7] and sub-rational reasoning [8,9]. Following the emergence of Artificial Neural Network (ANN) models, it was utilized in popularity as a prediction tool during the late 1980s.…”

A simple Mathematical Fuzzy Model of Brain Emotional Learning to Predict Kp Geomagnetic Index

“…A control algorithm based on LS model was introduced by Lucas et al [9][10] which is an action generation mechanism based on sensory inputs and emotional cues. Also LS model was proposed as an alarm system to predict the Kp index of geomagnetic activity [19][20][21][22] and to predict the AE index of space weather phenomena [23]. These indices characterize the solar winds and geomagnetic storms that is a complex system and can greatly disturb communication systems and damage satellites [23].…”