Conference Paper

Power management by brain emotional learning algorithm

Univ. of Tehran, Tehran
DOI: 10.1109/ICASIC.2007.4415571 Conference: ASIC, 2007. ASICON '07. 7th International Conference on
Source: IEEE Xplore


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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    • "where k is learning step, α and are learning rates and is decay rate in amygdala learning rule, where the k a k E t  is calculated error. This model has been used in various applications [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] and [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] and is developed here to predict the ozone level problem. "
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    ABSTRACT: The ozone level prediction is an important task of air quality agencies of modern cities. In this paper, we design an ozone level alarm system (OLP) for Isfahan city and test it through the real word data from 1-1-2000 to 7-6-2011. We propose a computer based system with three inputs and single output. The inputs include three sensors of solar ultraviolet (UV), total solar radiation (TSR) and total ozone (O3). And the output of the system is the predicted O3 of the next day and the alarm massages. A developed artificial intelligence (AI) algorithm is applied to determine the output, based on the inputs variables. For this issue, AI models, including supervised brain emotional learning (BEL), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs), are compared in order to find the best model. The simulation of the proposed system shows that it can be used successfully in prediction of major cities ozone level.
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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.
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    ABSTRACT: In this paper, a brain emotional learning based intelligent controller (BELBIC) is developed to control the switched reluctance motor (SRM) speed. Like other intelligent controllers, BELBIC is model free and is suitable to control nonlinear systems. Motor parameter changes, operating point changes, measurement noise, open circuit fault in one phase and asymmetric phases in SRM are also simulated to show the robustness and superior performance of BELBIC. To compare the BELBIC performance with other intelligent controllers, Fuzzy Logic Controller (FLC) is developed. System responses with BELBIC and FLC are compared. Furthermore, by eliminating the position sensor, a method is introduced to estimate the rotor position. This method is based on Adaptive Neuro Fuzzy Inference System (ANFIS). The estimator inputs are four phase flux linkages. Suggested rotor position estimator is simulated in different conditions. Simulation results confirm the accurate rotor position estimation in different loads and speeds.
    Energy Conversion and Management 01/2011; 52(1-52):85-96. DOI:10.1016/j.enconman.2010.06.046 · 4.38 Impact Factor
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