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您的检索式：作者名="LARGUECH Samia"
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| 1 | 利用碳纳米管-水纳米流体增强三角锥形太阳能蒸馏器的传热和传质显示文摘本文对碳纳米管-水纳米流体对三角锥形太阳蒸馏器内双扩散对流的影响进行了三维数值研究。研究了浮力比(-10≤N≤0)、纳米粒子体积分数(0≤Φ≤0.05)和瑞利数(103≤Ra≤105)等大范围调节参数的影响。得到流动结构、温度场和传热传质速率变化等结果。结果表明,浮力比可作为热质传递的优化参数,使用碳纳米管对太阳能蒸馏器的性能有积极的影响。 | GHACHEM Kaouther KOLSI Lioua LARGUECH Samia ALNEMER Ghada | 2021 | Journal of Central South University2021，28，11： | 2 |
| 2 | Performance Enhancement of Adaptive Neural Networks Based on Learning Rate显示文摘Deep learning is the process of determining parameters that reduce the cost function derived from the dataset.The optimization in neural networks at the time is known as the optimal parameters.To solve optimization,it initialize the parameters during the optimization process.There should be no variation in the cost function parameters at the global minimum.The momentum technique is a parameters optimization approach;however,it has difficulties stopping the parameter when the cost function value fulfills the global minimum(non-stop problem).Moreover,existing approaches use techniques;the learning rate is reduced during the iteration period.These techniques are monotonically reducing at a steady rate over time;our goal is to make the learning rate parameters.We present a method for determining the best parameters that adjust the learning rate in response to the cost function value.As a result,after the cost function has been optimized,the process of the rate Schedule is complete.This approach is shown to ensure convergence to the optimal parameters.This indicates that our strategy minimizes the cost function(or effective learning).The momentum approach is used in the proposed method.To solve the Momentum approach non-stop problem,we use the cost function of the parameter in our proposed method.As a result,this learning technique reduces the quantity of the parameter due to the impact of the cost function parameter.To verify that the learning works to test the strategy,we employed proof of convergence and empirical tests using current methods and the results are obtained using Python. | Swaleha Zubair Anjani Kumar Singha Nitish Pathak Neelam Sharma Shabana Urooj Samia Rabeh Larguech | 2023 | Computers, Materials & Continua2023，，1： | 0 |
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