Power management by brain emotional learning algorithm
ABSTRACT Nowadays having the most energy efficiency is desirable in its own right from both economical and environmental points of view. Dynamic power management is a system level solution for reducing the consumed energy with putting off unused parts of the system and putting them on in an efficient time. The Emotional Learning Algorithm has been introduced to show the effect of emotions as well known stimuli in the quick and almost satisfying decision making in human. The remarkable properties of emotional learning, low computational complexity and fast training, and its simplicity in multi objective problems has made it a powerful methodology in real time control and decision systems, where the gradient based methods and evolutionary algorithms are hard to be used due to their high computational complexity. Recently the emotional approach has been successfully used to obtain multiple objectives in prediction problems of real world phenomena. At first we introduce methods of dynamic power management and then a new method based on BELBIC would be explained. The simulation results show that this method has a high efficiency in various systems.
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ABSTRACT: In this paper we propose adaptive brain-inspired emotional decayed learning to predict Kp, AE and Dst indices that characterize the chaotic activity of the earth's magnetosphere by their extreme lows and highs. In mammalian brain, the limbic system processes emotional stimulus and consists of two main components: Amygdala and Orbitofrontal Cortex (OFC). Here, we propose a learning algorithm for the neural basis computational model of Amygdala–OFC in a supervised manner and consider a decay rate in Amygdala learning rule. This added decay rate has in fact a neurobiological basis and yields to better learning and adaptive decision making as illustrated here. In the experimental studies, various comparisons are made between the proposed method named ADBEL, Multilayer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Locally Linear Neuro-Fuzzy (LLNF). The main features of the presented predictor are the higher accuracy at all points especially at critical points, lower computational complexity and adaptive training. Hence, the presented model can be utilized in adaptive online prediction problems.Neurocomputing 01/2014; 126:188–196. · 2.01 Impact Factor
- Applied Artificial Intelligence 10/2014; 28(8):2014. · 0.48 Impact Factor
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ABSTRACT: In mammalians, brain Limbic System is responsible for emotionalprocess. An important part of motor learning as well as attention isformed by the system. Brain Emotional Learning (BEL) process ofmammalians has been mathematically modeled. Based on the model,a controller architecture called Brain Emotional Learning BasedIntelligent Controller (BELBIC) has been developed. EmotionalLearning is a powerful methodology in real time control anddecision systems due to low computational complexity and fasttraining where the gradient based methods and evolutionaryalgorithms are hard to be applied because of their highcomputational complexity. In this paper, the emotional processesand the Limbic System will be reviewed. In addition, the utilizationof BELBIC model in variety of applications of control system will beexamined. The results of simulations demonstrate control systemsbased on BELBIC have good performances.International Journal of Advances in Soft Computing and Its Applications. 01/2010;