在电动助力方向盘系统中,自学自适应神经模糊近似的强有力的控制行为
Tuan Anh Nguyen1,2, Tran Minh Ngoc Do3, Thi Thu Huong Tran2
1Faculty of Mechanical Engineering, Thuyloi University, Hanoi, Vietnam.
PloS one
|October 24, 2025
概括
这项研究介绍了一种混合适应性基于网络的模糊推理系统 (ANFIS) 算法,以克服人工智能 (AI) 数据训练挑战,如过拟. 新型ANFIS模型实现了对电动动力方向盘 (EPS) 系统的高精度和强大的概括性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 控制系统 控制系统
背景情况:
- 人工智能 (AI) 数据训练算法经常面临诸如过拟合,不足拟合和偏差等挑战.
- 现有的方法可能会在准确性和概括性方面扎,特别是在动态或新的场景中.
研究的目的:
- 设计和评估一种混合自学算法,以解决人工智能中常见的数据训练问题.
- 提高人工智能训练算法的准确性和概括能力.
主要方法:
- 通过将模糊逻辑和神经网络结构集成到基于自适应网络的模糊推理系统 (ANFIS) 中,开发了一种混合算法.
- 该ANFIS模型的设计有三个输入和一个输出.
- 训练模型使用来自电动助力方向盘 (EPS) 系统高性能稳健控制器的数据.
- 将拟议的ANFIS与反向传播神经网络 (BPNN) 作为基准进行比较.
主要成果:
- 拟议的ANFIS显示了高培训准确度,在训练良好的情况下,错误率低于1.7%.
- 保持了强大的插值能力,误差低于6.1%.
- 在培训领域之外的场景中,预测误差低于9.3%.
- 与基准BPNN相比,大大解决了过问题.
结论:
- 混合ANFIS算法提供卓越的数据训练准确性和概括性能.
- 这种方法有效地减轻了过度装配,超过了像BPNN这样的传统方法.
- 该ANFIS模型显示了如电动助力方向盘等系统中可靠的控制应用的前景.
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