一个混合获取ZNN与精确预定义的时间收时间变量LMVI及其应用UR机器人手臂和多代理系统的应用
IEEE transactions on neural networks and learning systems
|November 11, 2025
概括
没有非线性激活的新混合增益ZNN (HG-ZNN-WNAF) 精确地解决了时间变量线性矩阵向量不等式. 这种方法提供了精确的预定义时间融合和降低计算成本,优于现有的解决方案.
科学领域:
- 计算神经科学是一种计算神经科学.
- 人工智能的人工智能是人工智能.
- 应用数学 应用数学 应用数学
背景情况:
- 时间变量增益归零神经网络 (TVG-ZNN) 对时间变量线性矩阵向量不等式 (TVLMVI) 有效.
- 现有的具有非线性激活的TVG-ZNN具有高计算成本和不精确的融合时间,而线性激活变体缺乏有限时间的融合.
- 传统的TVG-ZNN中的增益值随着时间的推移而升级计算需求.
研究的目的:
- 为了解决TVLMVIs,引入一种没有非线性激活的新型混合增益ZNN (HG-ZNN-WNAF).
- 为了实现精确的预定义时间融合,并降低TVLMVI解决方案的计算成本.
- 为了确保在杂和无噪声环境中保持稳健性.
主要方法:
- 为基于线性激活函数的ZNN (HG-ZNN-WNAF) 设计一种新的混合增益.
- 确保混合增益价值仍然有限,以防止升级计算成本.
- 通过模拟和物理实验进行理论分析和验证.
主要成果:
- 在HG-ZNN-WNAF中,对TVLMVIs进行了精确的预定义时间收.
- 该模型实现了由于其碎片式混合增益和线性激活而降低了计算成本.
- 与最先进的方法相比,HG-ZNN-WNAF在融合速度,稳定性和计算效率方面表现出卓越的性能.
结论:
- 拟议的HG-ZNN-WNAF有效地解决了TVLMVI解决方案的现有TVG-ZNN的局限性.
- 精确的预定义时间融合和计算效率是新方法的关键优势.
- 在各种条件下,HG-ZNN-WNAF为TVLMVIs提供了强大的和计算效率高的解决方案.
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