I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

This chapter contains sections titled: Historical Review Supervised Multilayer Networks Unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation Summary References Recommended Exercises

Nonlinear System Identification

Dynamic Spectrum Access and Management in Cognitive Radio Networks: References

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Cognition Exploring The Science Of The Mind

Adaptive brain emotional decayed learning for online prediction of geomagnetic activity indices

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 neuro-fuzzy adaptive networks to mimic the functionality of brain emotional learning. In particular, the model is investigated to predict space storms, since the phenomenon has been recognised as a threat to critical infrastructure in modern society. To evaluate the performance of BELRFS, three benchmark time series: Lorenz time series, sunspot number time series and Auroral Electrojet (AE) index. The obtained results of BELRFS are compared with Linear Neuro-Fuzzy (LNF) with the Locally Linear Model Tree algorithm (LoLiMoT). The results indicate that the suggested model outperforms most of data driven models in terms of prediction accuracy.

/pdf/chaotic-time-series-prediction-using-brain-emotional-58lx1z9e1h.pdf

Chaotic time series prediction using Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS)

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 with fuzzy inference systems. The main aim of this model is presenting a desirable learning model for chaotic system prediction imitating the brain emotional network. In this research work, the model is used for predicting the solar activity, since it has been recognized as a threat to critical infrastructures in modern society. Specifically sunspot numbers are predicted by applying the proposed brain emotional learning model. The prediction results are compared with the outcomes of using other previous models like the locally linear model tree (LOLIMOT) and radial bias function (RBF) and adaptive neuro-fuzzy inference system (ANFIS).

Brain Emotional Learning Based Fuzzy Inference System (BELFIS) for Solar Activity Forecasting

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 a purposeful predicting system. The convergence theory is clarified by utilizing the model to predict the complex dynamical Lorenz system. Also the consequence of using this method to forecast such a complex system is compared with obtained results from other related studies that examine other methods such as Locally Linear Model Tree(LOLIMOT) and radial basis function (RBF) Neural network with Orthogonal lest square (OLS ) for predicting the Lorenz chaotic time series. The comparison indicates the superior performance of presented method to make the multi step ahead prediction. Also the effect of noise on the performance of the techniques is also considered. In deed, the learning methods could deal with predicting the future state of complex system with limited training data as well as large data set.

Reinforcement _recurrent fuzzy rule based system based on brain emotional learning structure to predict the complexity dynamic system

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 with a clustered network topology. A multi-objective function is designed to make a tradeoff between maximizing spectral utilization and minimizing interference. We compare the convergence behavior and performance of ACO-based method with obtained results from a grouping genetic algorithm (GGA).

/pdf/ant-colony-optimization-for-channel-assignment-problem-in-a-3lmaoxpval.pdf

Ant Colony Optimization for Channel Assignment Problem in a Clustered Mobile Ad Hoc Network

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 model based on local linear neuro-fuzzy models with linear model tree algorithm (LoLiMoT).

Mahboobeh Parsapoor

Papers

Chaotic time series prediction using Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS)

Brain Emotional Learning Based Fuzzy Inference System (BELFIS) for Solar Activity Forecasting

Reinforcement _recurrent fuzzy rule based system based on brain emotional learning structure to predict the complexity dynamic system

Ant Colony Optimization for Channel Assignment Problem in a Clustered Mobile Ad Hoc Network

Neuro-fuzzy models, BELRFS and LoLiMoT, for prediction of chaotic time series