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Impact of RTN on Pattern Recognition Accuracy of RRAM-Based Synaptic Neural Network

机译：RTN对基于RRAM的突触神经网络模式识别准确性的影响

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Zheng Chai; Pedro Freitas; Weidong Zhang; Firas Hatem; Jian Fu Zhang; John Marsland; Bogdan Govoreanu; Ludovic Goux; Gouri Sankar Kar;
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年度 2018
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1. Impact of RTN on Pattern Recognition Accuracy of RRAM-Based Synaptic Neural Network [J] . Zheng Chai, Pedro Freitas, Weidong Zhang, Electron Device Letters, IEEE . 2018,第11期

机译：RTN对基于RRAM的突触神经网络模式识别精度的影响
2. Impact of Synaptic Device Variations on Pattern Recognition Accuracy in a Hardware Neural Network [J] . Sungho Kim, Meehyun Lim, Yeamin Kim, Scientific reports. . 2018,第1期

机译：突触设备变化对硬件神经网络中模式识别精度的影响
3. Pattern Recognition of Spiking Neural Networks Based on Visual Mechanism and Supervised Synaptic Learning [J] . Xiumin Li, Hao Yi, Shengyuan Luo Journal of Neural Transplantation and Plasticity: Neural Plasticity . 2020,第Supplementa1期

机译：基于视觉机制和监督突触学习的尖峰神经网络的模式识别
4. Impact of RTN and Variability on RRAM-Based Neural Network [C] . P. Freitas, Z. Chai, W. Zhang, IEEE International Conference on Solid-State and Integrated Circuit Technology . 2020

机译：RTN对基于RRAM的神经网络的影响
5. Achieving Consistent Near-Optimal Pattern Recognition Accuracy Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks. [D] . Nikelshpur, Dmitry O. 2014

机译：使用粒子群优化对训练前的人工神经网络实现一致的近似最佳模式识别精度。
6. Impact of Synaptic Device Variations on Pattern Recognition Accuracy in a Hardware Neural Network [O] . Sungho Kim, Meehyun Lim, Yeamin Kim, -1

机译：突触设备变化对硬件神经网络中模式识别精度的影响
7. Impact of Synaptic Device Variations on Classification Accuracy in a Binarized Neural Network [O] . Sungho Kim, Hee-Dong Kim, Sung-Jin Choi 2019

机译：突触装置变化对二金属化神经网络中分类精度的影响
8. Recognition of subsurface defects in machined ceramics by application of neural networks to laser scatter patterns [R] . Stinson, M. C. , Lee, O. W. , Steckenrider, J. S. , 1994

机译：应用神经网络识别激光散射图案中机加工陶瓷表面下缺陷

Impact of RTN on Pattern Recognition Accuracy of RRAM-Based Synaptic Neural Network

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