一个新的固定时间错误监控神经网络,用于解决动态四次数值的西尔维斯特方程
Lin Xiao1, Penglin Cao1, Zidong Wang2
1Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, Hunan 410081, China.
概括
这项研究引入了一种新的神经网络,用于解决动态四次数值的西尔维斯特方程. 固定时间错误监控神经网络 (FTEMNN) 确保了快速,可靠的融合,并减少了聊天,在图像拒绝应用中证明有效.
科学领域:
- 计算数学是指计算数学.
- 人工智能的人工智能是人工智能.
- 图像处理 图像处理
背景情况:
- 动态四次数值的西尔维斯特方程 (DQSE) 提出了复杂的计算挑战.
- 现有的方法在解决这些方程时可能缺乏效率或稳定性.
研究的目的:
- 开发一种用于解决DQSE的新型神经网络模型.
- 为了实现固定时间的融合,并减轻聊天现象.
主要方法:
- 利用四实数表示法将DQSE转换为等价实数场方程.
- 提出了一个固定时间错误监控神经网络 (FTEMNN) 具有整体设计公式.
- 采用非线性激活函数用于固定时间收和和函数来处理聊天.
主要成果:
- FTEMNN模型证明了理论上的固定时间收.
- 通过数值示例验证模型的性能,突出其合性质.
- 成功地将FTEMNN应用于图像融合无线化,展示了实际的实用性.
结论:
- 拟议的FTEMNN为DQSE提供了有效和强大的解决方案.
- 该模型的固定时间收和聊天抑制提高了它的适用性.
- 在图像处理任务中证明了实用价值,例如denoising.
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