在光神经网络分类器中,关节损失函数的设计用于高功率效率的光学神经网络分类器
Optics express
|August 13, 2025
概括
一种新型的关节损失函数 (J-SCE) 显著提高了用于图像识别的衍射光学神经网络 (DONN) 的功率效率. 这一进步提高了DONN的稳定性和在计算机视觉任务中的实际应用.
科学领域:
- 光学是什么?光学是什么?光学是什么
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 衍射光学神经网络 (DONN) 提供高速,宽带宽和计算机视觉并行处理.
- 现有的DONN分类器在实际应用中面临功率效率的局限性.
研究的目的:
- 引入一个联合损耗函数 (J-SCE),以提高DONN的功率效率和分类性能.
- 提高DONN系统的能量定向能力和稳定性.
主要方法:
- 开发和实施一个联合损失函数 (J-SCE),集成分类准确性和衍射功率效率.
- 评估J-SCE函数对DONN功率效率和分类准确性的影响.
主要成果:
- 在DONN分类器功率效率方面取得了显著的改善,从0.92%提高到12.89%.
- 使用J-SCE函数保持了95.36%的高分类准确度.
- 证明了系统对噪声的强化稳定性和整体稳定性的提高.
结论:
- 通过优化能源分配,J-SCE功能有效地提高了DONN的功率效率.
- 这项工作对DONN分类器在图像识别和信息处理方面的实际实施作出了重大贡献.
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