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Neural Network Training By Gradient Descent Algorithms: Application on the Solar Cell

机译：梯度下降算法的神经网络训练：在太阳能电池上的应用

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Fayrouz Dkhichi; Benyounes Oukarfi;
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年度 2014
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1. Neural Network Training By Gradient Descent Algorithms: Application on the Solar Cell [J] . Fayrouz Dkhichi, Benyounes Oukarfi International Journal of Innovative Research in Science, Engineering and Technology . 2014,第8期

机译：梯度下降算法的神经网络训练：在太阳能电池上的应用
2. Robust Adaptive Gradient-Descent Training Algorithm for Recurrent Neural Networks in Discrete Time Domain [J] . Qing Song, Yilei Wu, Yeng Chai Soh IEEE Transactions on Neural Networks . 2008,第11期

机译：离散时域递归神经网络的鲁棒自适应梯度下降训练算法
3. Training qubit neural network with hybrid genetic algorithm and gradient descent for indirect adaptive controller design [J] . Soheil Ganjefar, Morteza Tofighi Engineering Applications of Artificial Intelligence . 2017,第octa期

机译：混合遗传算法和梯度下降训练Qubit神经网络用于间接自适应控制器设计
4. Multi-mother Wavelet Neural Network Training Using Genetic Algorithm-Based Approach to Optimize and Improves the Robustness of Gradient-Descent Algorithms: 3D Mesh Deformation Application [C] . Naziha Dhibi, Chokri Ben Amar International Work-Conference on Artificial Neural Networks . 2019

机译：基于遗传算法的多母波小波神经网络训练优化和提高梯度下降算法的鲁棒性：3D网格变形应用
5. Physiologically-based vision modeling applications and gradient descent-based parameter adaptation of pulse coupled neural networks. [D] . Broussard, Randy Paul. 1997

机译：基于生理的视觉建模应用和基于梯度下降的脉冲耦合神经网络参数自适应。
6. Mutual Information Based Learning Rate Decay for Stochastic Gradient Descent Training of Deep Neural Networks [O] . Shrihari Vasudevan 2020

机译：基于互动信息的学习速率衰减用于深神经网络的随机梯度血统训练
7. On Descent Spectral CG Algorithms for Training Recurrent Neural Networks [O] . I.E. Livieris, D.G. Sotiropoulos, P. Pintelas 2009

机译：关于训练复发性神经网络的下降光谱CG算法
8. Physiologically-Based Vision Modeling Applications and Gradient Descent-BasedParameter Adaptation of Pulse Coupled Neural Networks [R] . Broussard, R. P. 1997

机译：基于生理学的视觉建模应用和基于梯度下降的脉冲耦合神经网络参数自适应

Neural Network Training By Gradient Descent Algorithms: Application on the Solar Cell

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