This paper briefly describes how the neural structure of fear conditioning has inspired to develop a computational intelligence model that is referred to as the brain emotional learning-inspired model (BELIM). The model is applied to predict long step ahead of solar activity and geomagnetic storms. © Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Accurate prediction of solar activity as one aspect of space weather phenomena is essential to decrease the damage from these activities on the ground based communication, power grids, etc. Recently, the connectionist models of the brain such as neural networks and neuro-fuzzy methods have been proposed to forecast space weather phenomena; however, they have not been able to predict solar activity accurately. That has been a motivation for the development of the connectionist model of the brain; this paper aims to apply a connectionist model of the brain to accurately forecasting solar activity, in particular, solar cycle 24. The neuro-fuzzy method has been referred to as the brain emotional learning-based recurrent fuzzy system (BELRFS). BELRFS is tested for prediction of solar cycle 24, and the obtained results are compared with well-known neuro-fuzzy methods and neural networks as well as with physical-based methods. @2015 IEEE

One of the main challenges in the computational intelligence (CI) community is to develop nature-inspired algorithms that can efficiently solve real-world problems such as the prediction of space weather phenomena. An early example in this context is taking inspiration from the biological neurons in the mammal’s nervous system and developing an artificial neuron. This work laid the foundation for artificial neural networks (ANNs) that aim to mimic the connections between neurons in the mammal’s nervous system and to develop an artificial model of the brain. ANNs are well-known CI models that have shown high generalization capability when solving real-world problems, e.g., chaotic time-series prediction problems. However, ANNs mostly tend to suffer from long computation time and high model complexity. This thesis presents a new category of CI paradigms by taking inspiration from emotions, and these CI models are referred to as emotion-inspired computational intelligence models (EiCIs). In the thesis, I have outlined the preliminary steps that have been taken to develop EiCIs. These steps include studying different emotional theories and hypotheses, designing and implementing CI models for two specific applications in artificial intelligence (prediction and optimization), evaluating the performance of the new CI models, and comparing the obtained results with the results of well-known CI models (e.g., ANNs) and discussing the potential improvement that can be achieved. The first step, and a significant contribution of this thesis, is to review the various definitions of emotions and to investigate which emotional theories that are the most relevant for developing a CI model. Amongst different theories and hypotheses of emotions, the fear conditioning hypothesis as well as affect theory have been two main sources of inspiration in the development of the EiCIs proposed in this thesis. The fear conditioning hypothesis that was first proposed by LeDoux reveals some important characteristics of the underlying neural structure of fear conditioning behavior in biological systems. Based on the features of such networks, it could be an applicable hypothesis to be the basis of the development of a subgroup of EiCIs that could be used for prediction applications, e.g. BELIMs (Brain Emotional Learning Inspired Models), and as emotion-inspired engines for decision-making applications.The second emotional theory of the thesis is the affect theory (which was first suggested by Silvan Tomkins) that describes what the basic emotions are and how they can be associated with facial expressions. A mechanism to express the basic emotional feelings is also useful in designing another category of EiCIs that are referred to as emotion-inspired optimization methods. The fundamental hypotheses of the thesis, have led to developing EiCIs, can be presented as follows. The first hypothesis is that the neural structure of fear conditioning can be considered to be a nature-based system with the capability to show intelligent behavior through its functionality. This hypothesis is stated on the basis of the three main characteristics of the neural structure of fear conditioning behavior.The first characteristic is that the amygdala is the main center for processing fear-induced stimuli and that it provides the fear reaction through its interaction with other regions of the brain such as the sensory cortex, the thalamus, and the hippocampus. The second characteristic is that the procedure of processing of fearful stimuli and the provision of emotional reactions is simple and quick. The third aspect is that the amygdala not only provides fear responses but also learns to predict aversive events by interacting with other regions of the brain, which means that an intelligent behavior emerges.The second hypothesis is that the system in which the three monoamines neurotransmitters serotonin, dopamine, and noradrenalin and thus produces emotional behaviors, can be viewed as a biological system associated with the emergence of intelligent behavior.The above hypotheses state that a suitable way to develop new CI models is to take inspiration from the neural structure of fear conditioning and the natural system of three monoamine neurotransmitters. A significant contribution of this thesis is the evaluation of the ability of EiCIs by examining them to solve real-world problems such as the prediction of space weather phenomena (e.g., predicting real time-series such as sunspot number, auroral electrojet index, and disturbance time index) and the optimization of some central procedures in network communications. These evaluations have led to that comparable results have been obtained, which in turn supports the conclusion that EiCIs have acceptable and reasonable performance regarding computation time and model complexity. However, to achieve the final goal of the research study (i.e., to develop a CI model with low computation time and low model complexity), some enhancements of EiCIs are necessary. Moreover, new designs and implementations of these models can be developed by taking inspiration from other theories.

This paper introduces a new type of brain emotional learning inspired models (BELIMs). The suggested model is  utilized as a suitable model for predicting geomagnetic storms. The model is known as BELPM which is an acronym for Brain Emotional Learning-based Prediction Model. The structure of the suggested model consists of four main parts and mimics the corresponding regions of the neural structure underlying fear conditioning. The functions of these parts are implemented by assigning adaptive networks to the different parts. The learning algorithm of BELPM is based on the steepest descent (SD) and the least square estimator (LSE). In this paper, BELPM is employed to predict geomagnetic storms using the Disturbance Storm Time (Dst) index. To evaluate the performance of BELPM, the obtained results have been compared with the results of the adaptive neuro-fuzzy inference system (ANFIS). © 2014 Polish Information Processing Society.

This paper presents the results of applying a new clustering algorithm in ad hoc networks. This algorithm is a centralized method and is designed on the basis of an imperialist competitive algorithm (ICA). This algorithm aims to find a minimum number of cluster-heads while satisfying two constraints, the connectivity and interference. This work is a part of an ongoing research to develop a distributed interference aware cluster-based channel allocation method. As a matter of fact, the results of the centralized method are required to provide an upper level for the performance of the distributed version. The suggested method is evaluated for several scenarios and compares the obtained results with the reported results of ant colony optimization-based methods. © 2014 IEEE.

In this thesis the mammalian nervous system and mammalian brain have been used as inspiration to develop a computational intelligence model based on the neural structure of fear conditioning and to extend the structure of the previous proposed amygdala-orbitofrontal model. The proposed model can be seen as a framework for developing general computational intelligence based on the emotional system instead of traditional models on the rational system of the human brain. The suggested model can be considered a new data driven model and is referred to as the brain emotional learning-inspired model (BELIM). Structurally, a BELIM consists of four main parts to mimic those parts of the brain’s emotional system that are responsible for activating the fear response. In this thesis the model is initially investigated for prediction and classification. The performance has been evaluated using various benchmark data sets from prediction applications, e.g. sunspot numbers from solar activity prediction, auroral electroject (AE) index from geomagnetic storms prediction and Henon map, Lorenz time series. In most of these cases, the model was tested for both long-term and short-term prediction. The performance of BELIM has also been evaluated for classification, by classifying binary and multiclass benchmark data sets.

This paper suggests a new engine to be used to develop cognitive nodes in cognitive radio networks. Instead of the traditional cognitive cycle, the suggested engine could be designed based on an emotional cycle that is inspired by the emotional system that reacts to the received stimulus and learns from the reaction. The engine is called ELIE that stands for Emotional Learning Inspired Engine. This paper presents the structure of ELIE and explains how it can be implemented on the basis of generic policy architecture. This paper also discusses the possible applications of the suggested engine.

This study presents comparative results obtained from employing four different neuro-fuzzy models to predict geomagnetic storms. Two of these neuro-fuzzy models can be classified as Brain Emotional Learning Inspired Models (BELIMs). These two models are BELFIS (Brain Emotional Learning Based Fuzzy Inference System) and BELRFS (Brain Emotional Learning Recurrent Fuzzy System). The two other models are Adaptive Neuro-Fuzzy Inference System (ANFIS) and Locally Linear Model Tree (LoLiMoT) learning algorithm, two powerful neuro-fuzzy models to accurately predict a nonlinear system. These models are compared for their ability to predict geomagnetic storms using the AE index.

This paper presents new channel assignment algorithm for a clustered ad hoc network. The suggested method is based on a graph-theoretic model and seeks a solution for the channel assignment problem in a clustered ad hoc network. The method is based on a new meta-heuristic algorithm that is referred to as imperialist competitive algorithm (ICA). It provides a scheme for allocating the available channels to the cluster heads, maximizing spectrum efficiency and minimizing co-channel interference. The suggested method is tested for several scenarios and its performance is compared with a genetic algorithm based scheme. © 2013 IEEE

In this paper we propose an architecture for a cognitive engine that is based on the emotional learning cycle instead of the traditional cognitive cycle. The cognitive cycle that traditionally has been used as reference for cognitive radio is on the basis of the Unified Theories of Cognition (UTC) to model rational decision making in humans. UTC represents a rational goal-oriented decision-action made by an intelligent agent. However, the emotional cycle represents an emotional reaction-oriented cycle instead. These two models differ in function and structure of learning, decision making and optimization. In this work the structure of these two learning cycles are compared and a computational model for artificial emotional learning based engine is suggested.