一个近位神经动力学模型,用于非线性反向混合变异不等式的系统
Anjali Upadhyay1, Rahul Pandey2
1Department of Mathematics, University of Delhi, Delhi, India.
概括
本研究引入了一种新的近接神经动力学模型 (PNDM),用于解决非线性反向混合变异不等式 (SNIMVIs). 该模型在特定条件下证明了解决方案的独特性和稳定性,并提供了一种代算法.
科学领域:
- 应用数学 应用数学 应用数学
- 计算科学 计算科学
- 优化理论 优化理论
背景情况:
- 变量不等式在优化和应用数学中是基本的.
- 解决复杂的非线性系统需要强大的数值方法.
- 现有的方法可能会面临非单调或非利普希茨条件的挑战.
研究的目的:
- 引入一个非线性反向混合变异不等式 (SNIMVIs) 系统.
- 提出和分析用于解决SNIMVIs的近位神经动力学模型 (PNDM).
- 为SNIMVIs开发一个代算法.
主要方法:
- 在神经动力学框架内利用近距离映射.
- 通过利普希茨连续性假设证明解决方案的独特性.
- 使用利普希茨连续性和强单调性建立全球的非对称稳定性.
主要成果:
- 证明了PNDM连续解决方案的独特性.
- 在特定条件下建立了平衡点的全球非对称稳定性.
- 介绍了一种代算法,并分析了一个违反强单调性的案例.
结论:
- 拟议的PNDM对于在利普希茨连续性和强单调性下解决SNIMVIs是有效的.
- 该研究强调了模型的行为,包括分歧,当条件不满足时.
- 这些发现为解决复杂的不平等系统提供了一个新的计算工具.
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