基于新型pi-sigma神经网络的动态系统识别,使用利亚普诺夫稳定性分析
Richa Sahu1, Rajesh Kumar2, Smriti Srivastava3
1Department of Electrical Engineering, Netaji Subhas University of Technology, New Delhi, India.
ISA transactions
|November 2, 2025
概括
一个新的对角分层Pi-Sigma神经网络 (DLPSNN) 能够有效地识别非线性动态系统. 与其他神经网络相比,这种反复复的模型显示出更高的准确性和稳定性.
科学领域:
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
- 动态系统理论 动态系统理论
背景情况:
- 非线性动态系统对建模具有重大挑战.
- 现有的神经网络架构在捕捉复杂动态方面存在局限性.
- 皮西格玛神经网络 (PSNN) 提供了增强的非线性建模功能.
研究的目的:
- 介绍了一种新的循环神经网络,即对角分层Pi-Sigma神经网络 (DLPSNN).
- 使用DLPSNN增强非线性动态系统的建模.
- 评估拟议的DLPSNN模型的稳定性和稳定性.
主要方法:
- 通过调整PSNN并添加额外的反层来开发DLPSNN.
- 使用背向传播 (BP) 算法进行体重更新.
- 通过使用Lyapunov-Stability (LS) 原则评估模型稳定性.
主要成果:
- DLPSNN在识别非线性动态系统方面表现出卓越的表现.
- 与PSNN,FNN,ENN,DNN和JNN相比,该模型实现了更高的输出精度和最小化的错误.
- DLPSNN从扰动中表现出强大的恢复.
结论:
- DLPSNN是用于非线性动态系统识别的高效反复模型.
- 与现有的神经网络模型相比,DLPSNN在准确性和稳定性方面提供了显著的改进.
- 该模型的稳定性和弹性使其适合复杂的现实应用.
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