一个增强的低计算复杂度预定义时间的收式零化神经网络,用于受约束的时间变化的二次编程,用于机器人操纵器的动力控制
IEEE transactions on neural networks and learning systems
|March 13, 2026
概括
本研究介绍了一种增强的低计算复杂度归零神经网络 (ELNCP-LCCZNN) 模型,以有效地解决复杂的时间变量二次编程 (TVQP) 问题. 新模型提高了计算效率和噪声强度,用于现实世界的工程应用.
科学领域:
- 控制系统工程 控制系统工程
- 优化理论 优化理论
- 计算神经科学是一种神经科学.
背景情况:
- 时间变化的二次编程 (TVQP) 问题在工程中很普遍,但也带来了计算方面的挑战.
- 现有的基于非线性互补性问题 (NCP) 的归零神经网络 (ZNN) 在效率和噪声强度方面面临限制.
- 这些局限性源于矩阵尺寸的增加,对矩阵倒置的依赖,以及对噪声的敏感性.
研究的目的:
- 开发一个增强的低维NCP低计算复杂性的ZNN (ELNCP-LCCZNN) 模型.
- 为解决TVQP的传统ZNN的计算低效和噪声敏感性.
- 为TVQP提供实时解决方案,具有时间变化的平等,不平等和边界约束.
主要方法:
- 设计了一个增强的非线性互补性问题 (ELNCP) 函数来减少模型尺寸.
- 采用低计算复杂性的ZNN (LCCZNN) 框架来消除矩阵反转.
- 包含了一个非线性激活功能,用于预定义的时间收和噪声弹性.
主要成果:
- 根据ELNCP-LCCZNN模型,计算复杂性降低,噪声强度提高.
- 数字模拟和机器人操纵机动控制实验验验证了模型的性能.
- 拟议的模型实现了比现有方法更好的计算效率和实际实施性.
结论:
- ELNCP-LCCZNN模型为解决具有复杂约束的TVQP问题提供了一种优越的方法.
- 该模型在速度,准确性和稳定性方面提供了增强的性能.
- 这一进步对机器人和其他工程领域的实时控制应用有重大影响.
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