修改的噪音免疫模糊神经网络用于解决方程编程与平等约束问题的问题.
概括
这项研究引入了一种修改的噪声免疫模糊神经网络 (MNIFNN) 来解决具有平等约束 (QPEC) 的二次编程问题. 与现有方法相比,新型MNIFNN模型表现出优越的噪声耐受性和稳定性.
科学领域:
- 优化优化 优化优化
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 具有平等约束的二次编程 (QPEC) 问题广泛应用于各种行业.
- 噪音干扰在解决现实应用中的QPEC问题方面构成了重大挑战.
- 现有的循环神经网络模型经常与噪音干扰作斗争.
研究的目的:
- 提出一种新的修改噪声免疫模糊神经网络 (MNIFNN) 模型.
- 为了提高基于神经网络的QPEC解决方案的耐噪声和稳定性.
- 通过模糊逻辑来提高QPEC解决模型的适应性.
主要方法:
- 修改噪声免疫模糊神经网络 (MNIFNN) 的开发.
- 整合比例,积分和差异元素,以提高强度.
- 利用两个模糊逻辑系统 (FLS) 来生成基于残余和残余积分项的自适应设计参数.
主要成果:
- 拟议的MNIFNN模型具有固有的噪声耐受性.
- 与传统梯度循环神经网络 (TGRNN) 和传统归零循环神经网络 (TZRNN) 模型相比,MNIFNN表现出优越的稳定性.
- 数字模拟证实了MNIFNN在处理噪声干扰方面的有效性.
结论:
- MNIFNN是解决QPEC问题的有效和强大的模型,特别是在杂的环境中.
- 适应性模糊参数设计提高了该模型的适用性.
- 在噪音敏感的QPEC应用中,MNIFNN为现有方法提供了一个有希望的替代方案.
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