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Updated: Mar 7, 2026

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基于稳定方法的对角反复量子神经网络用于识别非线性系统

Hossam Khalil1,2, Osama Elshazly3,2, Omar Shaheen4

  • 1Mechatronics Engineering Department, College of Engineering, October 6 University, Giza, Egypt.

Scientific reports
|March 5, 2026
PubMed
概括

一个新的带有利亚普诺夫稳定性的对角反复量子神经网络 (DRQNN-LS) 有效地模拟了复杂的非线性动态. 这种量子方法确保了现实世界系统的稳定融合和强大的性能.

关键词:
横向的反复量子神经网络是对角的.动态系统识别 动态系统识别利亚普诺夫稳定性分析量子神经网络是一个量子神经网络.

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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