强大的自我组织模糊神经网络与数据免疫性评估,用于工业过程建模
Zheng Liu1, Guoqing Cai1, Honggui Han1
1School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China; Engineering Research Center of Digital Community Ministry of Education, Beijing University of Technology, Beijing 100124, China.
概括
一个具有数据免疫性评估 (RSOFNN-DIE) 的新强大的自我组织模糊神经网络增强了工业过程建模. 这种方法通过有效处理杂和不确定的数据来提高准确性和稳定性.
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:
- 神经网络提供了动态的适应性,但与工业数据的不确定性,异常值和噪音作斗争.
- 由于工业应用中的数据质量问题,现有模型在促进准确性和稳定性方面面临限制.
研究的目的:
- 开发一个强大的自我组织的模糊神经网络与数据免疫性评估 (RSOFNN-DIE) 用于工业过程建模.
- 通过解决数据不确定性,异常值和噪声来提高模型的稳定性和准确性.
主要方法:
- 实施数据免疫性评估策略,以确定网络结构并提高稳定性.
- 引入了一个参数学习算法,具有评估惩罚机制,以适应模型参数的输入数据变化.
- 从理论上分析了RSOFNN-DIE模型的融合和稳定性.
主要成果:
- 在工业过程建模中,RSOFNN-DIE表现出卓越的模型性能.
- 拟议的模型有效地减轻了对异常值和噪声的敏感性,改善了反干扰能力.
- 理论验证证实了RSOFNN-DIE在杂环境中的有效实施.
结论:
- RSOFNN-DIE为工业过程建模提供了强大而准确的解决方案,即使数据不确定.
- 开发的数据免疫性评估和参数学习策略显著提高了模型性能.
- 这些发现支持RSOFNN-DIE在具有挑战性的工业环境中的实际应用.
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