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Control and Mitigation Strategies for Fractional Order ENSO Model via Artificial Neural Network Approach |
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PP: 191-209 |
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doi:10.18576/pfda/120113
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Author(s) |
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Jagdev Singh,
Rakesh Saini,
Dumitru Baleanu,
Devendra Kumar,
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Abstract |
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| The current investigation is related to solve the fractional El Nin ̃o-Southern Oscillation (ENSO) model by using the Gudermannian neural network approach. The network weights are optimized using a hybrid genetic algorithm with an interior point algorithm (GAIPA). An error function is defined for ENSO with its initial conditions and optimized its weights using GAIPA. Classical and fractional order ENSO models are solved with different parameter values. The convergence measures in the sense of root mean square error (RMSE), mean absolute deviation (MAD), and Theil’s inequality coefficient (TIC) are also discussed and prove the effectiveness of the proposed method.
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