Chaotic time series prediction using Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS)
TLDR
An architecture based on the anatomical structure of the emotional network in the brain of mammalians is applied as a prediction model for chaotic time series studies to predict space storms.Abstract:
In this paper, an architecture based on the anatomical structure of the emotional network in the brain of mammalians is applied as a prediction model for chaotic time series studies. The architecture is called Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS), which stands for: Brain Emotional Learning-based Recurrent Fuzzy System. It adopts neuro-fuzzy adaptive networks to mimic the functionality of brain emotional learning. In particular, the model is investigated to predict space storms, since the phenomenon has been recognised as a threat to critical infrastructure in modern society. To evaluate the performance of BELRFS, three benchmark time series: Lorenz time series, sunspot number time series and Auroral Electrojet (AE) index. The obtained results of BELRFS are compared with Linear Neuro-Fuzzy (LNF) with the Locally Linear Model Tree algorithm (LoLiMoT). The results indicate that the suggested model outperforms most of data driven models in terms of prediction accuracy.read more
Citations
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Principles of Neural Science
TL;DR: The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or her own research.
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An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification
TL;DR: Comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.
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An integrated chaotic time series prediction model based on efficient extreme learning machine and differential evolution
Wei Guo,Tao Xu,Zonglei Lu +2 more
TL;DR: Experimental results show that the proposed integrated prediction model can not only provide stable prediction performances with high efficiency but also achieve much more accurate prediction results than its counterparts for chaotic time series prediction.
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Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.
TL;DR: A novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) is proposed that incorporates higher order terms, recurrence and error feedback and shows an average 23.34% improvement in Root Mean Square Error with respect to RPNN and an average 10.74% improvement to DRPNN, indicating that using network errors during training helps enhance the overall forecasting performance for the network.
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A novel error-output recurrent neural network model for time series forecasting
Waddah Waheeb,Rozaida Ghazali +1 more
TL;DR: Simulation results have shown that RPNN-EOF is the most accurate model among all the compared models with the time series used, which shows that employing auto-regressive and moving-average inputs together helps to produce more accurate forecasts.
References
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Principles of Neural Science
TL;DR: The principles of neural science as mentioned in this paper have been used in neural networks for the purpose of neural network engineering and neural networks have been applied in the field of neural networks, such as:
Journal ArticleDOI
Principles of Neural Science
TL;DR: The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or her own research.
Book
Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence
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Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]
TL;DR: Interestingly, neuro fuzzy and soft computing a computational approach to learning and machine intelligence that you really wait for now is coming.
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TL;DR: A circular cribbage board having a circular base plate on which a circular counter disc, bearing a circular scale having 122 divisions numbered consecutively from 0, is mounted for rotation.