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: The amygdala has repeatedly been impli- cated in emotional reactions and in learn- ing of new emotionally significant stimuli. The system forms an important part of mo- tor learning as well as attention. This pa- per presents a neurologically inspired com- putational model of the amygdala and the orbitofrontal cortex that aims to partially re- produce the same characteristics as the bio- logical system. This model has been tested in simulations, the results of which are pre- sented.Cybernetics and Systems. 01/2001; 32:611-636.
- 01/1998; Kluwer., ISBN: 978-0-7923-8086-3
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ABSTRACT: In this paper a new control strategy based on Brain Emotional Learning (BEL) model has been introduced. A modified BEL model has been proposed to increase the degree of freedom, controlling capability, reliability and robustness, which can be implemented in real engineering systems. The performance of the proposed BEL controller has been illustrated by applying it on different nonlinear uncertain systems, showing very good adaptability and robustness, while maintaining stability.