NDLI: Fossil fuel consumption prediction using emotional learning in Amygdala

Content Provider	IEEE Xplore Digital Library
Author	Ayanzadeh, R. Mousavi, A.S.Z. Setayeshi, S.
Copyright Year	2011
Description	Author affiliation: Computer. Department, Science and Research Branch, Islamic Azad University, Tehran, Iran (Mousavi, A.S.Z.) \|\| Nuclear Engineering Department, Amirkabir University of Technology, Tehran, Iran (Setayeshi, S.) \|\| Young Researchers Club, Science and Research Branch, Islamic Azad University, Tehran, Iran (Ayanzadeh, R.)
Abstract	Fossil fuels are precious limited sources of energy that are sorely vital for humanity, so it has always been emphasized and worldwide attention have widely focused on the issue. Excessive use of fossil fuels due to industrial developments in recent years has caused serious problems regarding ecology, environment and resource management, as far as it made global challenges to control the consumption of fossil fuels. This research has accomplished to predict global fossil fuel consumption in coming decays. The records of data from global usage, indicates intrinsic chaotic behaviour of the data, therefore anticipation seems to be more difficult to implement it with conventional tools of time series prediction. In this paper a new approach is proposed as Amygdala-Orbitofrontal emotional learning model, to foresight the universal trend of fossil fuel consumption. Simulation results prove that the applied method has prominent capability in forecasting chaotic time series. Thus, it can be claimed that the ultimate results is admissible for future works.
Starting Page	1
Ending Page	6
File Size	331015
Page Count	6
File Format	PDF
ISBN	9781457707308
e-ISBN	9789644634284
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-17
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	AmirKabir Univ of Tech
Subject Keyword	Chaos Computational modeling Emotional Learning Prediction Predictive models Time Series Brain modeling Fossil fuels Global warming Mathematical model Fossil Fuel
Content Type	Text
Resource Type	Article

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