History matching and uncertainty quantification for velocity dependent relative permeability parameters in a gas condensate reservoir

Farahani, Zohreh Dermanaki; Ahmadi, Mehdi; Sharifi, Mohammad

doi:10.1007/s12517-019-4603-x

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…With the rapid development of powerful computing environments and rich data sources, artificial intelligence (AI) technology such as neural networks [1][2][3], adaptive filtering [4][5][6] and evolutionary algorithms [7][8][9] has become increasingly more applicable for forecasting problems in various scenarios, such as medicine [10][11][12], economy [13][14][15] and electronic engineering [16][17][18]. The methods have acquired high reputations due to their great approximation abilities.…”

Section: Introductionmentioning

confidence: 99%

Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction

Mei

Liu

et al. 2019

Entropy

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In recent years, the correntropy instead of the mean squared error has been widely taken as a powerful tool for enhancing the robustness against noise and outliers by forming the local similarity measurements. However, most correntropy-based models either have too simple descriptions of the correntropy or require too many parameters to adjust in advance, which is likely to cause poor performance since the correntropy fails to reflect the probability distributions of the signals. Therefore, in this paper, a novel correntropy-based extreme learning machine (ELM) called ECC-ELM has been proposed to provide a more robust training strategy based on the newly developed multi-kernel correntropy with the parameters that are generated using cooperative evolution. To achieve an accurate description of the correntropy, the method adopts a cooperative evolution which optimizes the bandwidths by switching delayed particle swarm optimization (SDPSO) and generates the corresponding influence coefficients that minimizes the minimum integrated error (MIE) to adaptively provide the best solution. The simulated experiments and real-world applications show that cooperative evolution can achieve the optimal solution which provides an accurate description on the probability distribution of the current error in the model. Therefore, the multi-kernel correntropy that is built with the optimal solution results in more robustness against the noise and outliers when training the model, which increases the accuracy of the predictions compared with other methods.

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