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Register a new userA Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer
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Added by
Tarik A. Rashid
Publication date
2019
fields
Informatics Engineering
and research's language is
English
Authors
Tarik A. Rashid
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Identifying university students weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students outcomes. This proposed system would improve instruction by the faculty and enhance the students learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.
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Foresight of CO$_2$ emissions from fuel combustion is essential for policy-makers to identify ready targets for effective reduction plans and further to improve energy policies and plans. For the purpose of accurately forecasting the future development of Chinas CO$_2$ emissions from fuel combustion, a novel continuous fractional nonlinear grey Bernoulli model is developed in this paper. The fractional nonlinear grey Bernoulli model already in place is known that has a fixed first-order derivative that impairs the predictive performance to some extent. To address this problem, in the newly proposed model, a flexible variable is introduced into the order of derivative, freeing it from integer-order accumulation. In order to further improve the performance of the newly proposed model, a meta-heuristic algorithm, namely Grey Wolf Optimizer (GWO), is determined to the emerging coefficients. To demonstrate the effectiveness, two real examples and Chinas fuel combustion-related CO$_2$ emissions are used for model validation by comparing with other benchmark models, the results show the proposed model outperforms competitors. Thus, the future development trend of fuel combustion-related CO$_2$ emissions by 2023 are predicted, accounting for 10039.80 Million tons (Mt). In accordance with the forecasts, several suggestions are provided to curb carbon dioxide emissions.
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