Mahboobeh Parsapoor

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In this study, new approach based on brain emotional Learning process is presented to predict chaotic system more accurate than other learning models. So the main scope of this paper is to reveal the advantages of this learning model that imitate the internal representation of brain emotional learning model to provide a correct response to stimuli to state(More)
This paper presents a new architecture based on a brain emotional learning model that can be used in a wide varieties of AI applications such as prediction, identification and classification. The architecture is referred to as: Brain Emotional Learning Based Fuzzy Inference System (BELFIS) and it is developed from merging the idea of prior emotional models(More)
This paper suggests a novel learning model for prediction of chaotic time series, brain emotional learning-based recurrent fuzzy system (BELRFS). The prediction model is inspired by the emotional learning system of the mammal brain. BELRFS is applied for predicting Lorenz and Ikeda time series and the results are compared with the results from a prediction(More)
In this paper, an architecture based on the anatomical structure of the emotional network in the brain of mammalians is applied as a prediction model for chaotic time series studies. The architecture is called Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS), which stands for: Brain Emotional Learning-based Recurrent Fuzzy System. It adopts(More)
In this paper, we suggest an inspired architecture by brain emotional processing for classification applications. The architecture is a type of ensemble classifier and is referred to as `emotional learning-inspired ensemble classifier' (ELiEC). In this paper, we suggest the weighted k-nearest neighbor classifier as the basic classifier of ELiEC. We evaluate(More)
This paper presents a modified model of brain emotional learning based fuzzy inference system (BELFIS). It has been suggested to predict chaotic time series. We modify the BELFIS model merging radial basis function network with adaptive neuro-fuzzy network. The suggested model is evaluated by testing on complex systems and the obtained results are compared(More)
In this paper a novel optimization method called imperialist competitive algorithm (ICA) is applied to solve the channel assignment problem in a mobile ad hoc network. First the imperialist competitive algorithm (ICA) is described, which has been proposed as an evolutionary optimization method, and after that it is explained how it can seek a near optimal(More)
This paper presents an ant colony optimization (ACO) method as a method for channel assignment in a mobile ad hoc network (MANET), where achieving high spectral efficiency necessitates an efficient channel assignment. The suggested algorithm is intended for graph-coloring problems and it is specifically tweaked to the channel assignment problem in MANET(More)