Fossil fuel consumption prediction using emotiona learning in Amygdala

Summary from only given. 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 behavior 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.