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建模、优化、故障诊断是流程工业CIMS技术中的关键技术。传统的建模、优化、故障诊断方法依赖于数学模型仿真或专家经验规则 ,对于强非线性和非高斯分布噪声的对象存在着知识获取瓶颈。而数据挖掘技术综合运用机器学习、计算智能 (人工神经网、遗传算法 )、模式识别、数理统计等技术 ,从大量数据中挖掘和发现有价值和隐含的知识。本文进一步研究了建模、优化、故障诊断的数据挖掘系统 ,以及规则挖掘、参变量优化、故障诊断建模的算法。
Modeling, optimization, fault diagnosis is the key technology in process industry CIMS technology. The traditional methods of modeling, optimization and fault diagnosis rely on mathematical model simulation or expert experience rules, and have bottlenecks for knowledge acquisition of strongly nonlinear and non-Gaussian distributed noise objects. However, data mining techniques use techniques such as machine learning, computational intelligence (artificial neural network, genetic algorithm), pattern recognition and mathematical statistics to discover and discover valuable and implicit knowledge from a large amount of data. This paper further studies the data mining system of modeling, optimization and fault diagnosis, as well as the algorithm of rule mining, parameter variable optimization and fault diagnosis modeling.