两个新的冷启动多阶段神经解答器,用于具有延长时间视界的受约束非线性方程
Qiuyue Zuo1, Haibing Fan1, Lin Xiao1
1Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, 410081, China.
我们介绍了冷启动的多阶段归零神经解答器 (CM-ZNSs) 来解决具有挑战性的非线性方程. 与以前的方法相比,CM-ZNS提供了更好的持续时间,准确性和初始条件独立性.
科学领域:
- * 计算数学 计算机数学
- * 神经网络动力学
- * 机器人和控制系统
背景情况:
- * 线性受约束的非线性方程系统 (LC-SNEs) 在机器人技术等工业应用中至关重要.
- *对于LC-SNEs而言,现有的归零神经动力学 (ZND) 解决方案通常存在较短的操作持续时间和对初始条件的敏感性.
- * 传统ZND质量矩阵的局限性阻碍了它们的长期性能和稳定性.
研究的目的:
- *为LC-SNEs开发一种新的解决方法,克服现有方法的局限性.
- * 引入冷启动多阶段归零神经解决器 (CM-ZNS),以提高解决LC-SNEs的性能.
- * 为了提高 LC-SNEs 神经解决器的时间持续时间,收速度,耐噪,和计算精度.
主要方法:
- * 设计和实施五个单阶段的归零神经解决器 (SS-ZNSs) 来解决目标LC-SNE.
- * 开发冷启动多阶段归零神经解决器 (CM-ZNS) 框架,包括冷启动阶段,以改善初始条件.
- *在多个阶段采用不同ZND的战略使用,优先考虑冷启动,融合,精度和耐噪.
- *利用基于模糊逻辑的方法自动调整冷启动参数,以确保所需的解决方案时间持续时间.
主要成果:
- *CM-ZNS在所有评估的指标上表现优于SS-ZNS:时间持续时间,收速度,噪声耐受性和计算精度.
- *冷启动阶段有效地建立了一个合理的初始解决方案,减少对初始条件的依赖.
- * 严格的分析和比较实验证实了拟议的CM-ZNSs的增强能力.
结论:
- * 拟议的CM-ZNS方法代表了在解决非线性方程的线性受约束系统方面取得的重大进展.
- * CM-ZNS 与传统的单阶段解决方案相比,提供了更强大,更准确和更长时间的解决方案.
- * 这项工作为在复杂的工业问题中更可靠,更高效地应用神经解决器铺平了道路.
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