An Improved Brain Emotional Learning Model Inspired By Online Recurrent Memory Sequential Fuzzy Extreme Learning Machine Based On TSK Fuzzy Inference System

Document Type : Original Article

Authors

Iran

Abstract

Prediction models and classification algorithms are widely used in many science and technology. Among their various methods, Well-known data-driven methods such as neural networks and neuro-fuzzy models because of their characteristics have been considered by many researchers. To develop and overcome the weak points of these models, the concepts of the human brain biological systems are used. Therefore, the brain's emotional limbic system is used to develop these models. Brain Emotional Learning (BEL) is an emotional artificial neural network based on the interaction of the thalamus, cortex, amygdala, and orbitofrontal components. This learning machine has different architectures and learning algorithms.
In this paper, the online fuzzy extreme learning machine is used as the amygdala and orbitofrontal component in the brain emotional learning machine. To interact between the main components of the brain emotional learning machine, online recurrent memory sequential fuzzy extreme learning machine with different memory depth and transfer learning ability is used. The final design machine is called Brain Emotional Learning based on Online Recurrent Memory Sequential Fuzzy Extreme Learning Machine (BEL-ORMS-FELM). The proposed cognitive machine is designed based on learning the training data one-by-one but also chunk-by-chunk (with fixed or varying length) and it can discard training data that has already been trained. Performance comparison of the proposed method is done with other similar learning methods on the benchmark problems of chaotic time series. The results of analysis and simulations show that the performance and accuracy of the proposed method are higher than other methods.

Keywords

References

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Fuzzy Systems and its Applications
Volume 4, Issue 1 - Serial Number 7
November 2021
Pages 47-78

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History

  • Receive Date: 17 July 2021
  • Revise Date: 19 August 2021
  • Accept Date: 20 September 2021

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