Emotion-based control systems

Ventura, Rodrigo; Pinto-Ferreira, Carlos

doi:10.1109/isic.1999.796631

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…the high values of AE related to geomagnetic storms or sub storms. 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 multi objective real time control and decision systems, where the gradient based methods and evolutionary algorithms are characterized by high computational complexity (Fatourechi et al 2001;Perlovsky 1999;Ventura and Pinto Ferreira 1999). However this learning algorithm is far from an optimal approximation, it is useful to warning and alert systems due to its high rate of correct warning messages in comparison with several other approaches.…”

Section: Introductionmentioning

confidence: 99%

Learning based brain emotional intelligence as a new aspect for development of an alarm system

2007

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The multi criteria and purposeful prediction approach has been introduced and is implemented by the fast and efficient behavioral based brain emotional learning method. On the other side, the emotional learning from brain model has shown good performance and is characterized by high generalization property. New approach is developed to deal with low computational and memory resources and can be used with the largest available data sets. The scope of paper is to reveal the advantages of emotional learning interpretations of brain as a purposeful forecasting system designed to warning; and to make a fair comparison between the successful neural (MLP) and neurofuzzy (ANFIS) approaches in their best structures and according to prediction accuracy, generalization, and computational complexity. The auroral electrojet (AE) index are used as practical examples of chaotic time series and introduced method used to make predictions and warning of geomagnetic disturbances and geomagnetic storms based on AE index.

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Section: Introductionmentioning

confidence: 99%

Learning based brain emotional intelligence as a new aspect for development of an alarm system

2007

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“…As can be seen these properties make the method preferable in real time applications like control tasks, as have been presented in literature [3], [4], [5], [9], [12], [18]. For one thing, one can use very complicated definitions for emotional signal without increasing the computational complexity of algorithm or worrying about differentiability or renderability into recursive formulation problems.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear System Identification Using ANFIS Based on Emotional Learning

Jalili-Kharaajoo

2004

Lecture Notes in Computer Science

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In this paper using emotional learning, the performance of Adaptive Network Fuzzy Inference System (ANFIS) will be enhanced. Neural networks and Neurofuzzy models have been successfully used in nonlinear time series prediction. Many of the existing methods for learning algorithm of these networks, constructed over Takagi Sugeno fuzzy inference system, are characterized by high generalization. However, they differ in computational complexity. A practical approach towards the prediction of real world data such as the sunspot number time series profound a method to follow multiple goals. For example predicting the peaks of sunspot numbers (maximum of solar activity) is more important due to its major effects on earth and satellites. The proposed Emotional Learning Based Fuzzy Inference System (ELFIS) has the advantage of low computational complexity in comparison with other multiobjective optimization methods.

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