【摘要】 为了有效地利用经验知识,弥补训练数据覆盖范围不足的问题,提出一种将经验知识以TSK(Takagi-Sugeno-Kang)型模糊规则引入模糊模型的建模方法.在结构辨识中,提出了模糊规则融合方法,用以确定初始模糊规则.在参数辨识中,改进了原梯度下降方法中的目标函数,并引入了经验知识准确性评价参数,用以平衡样本数据和经验知识对模型的影响.数值仿真和工程实例应用结果表明,所提出的方法可以有效地利用经验知识和样本数据,使预报结果更可靠、更精确.更多还原

【Abstract】 To effectively use the empirical knowledge to compensate for incomplete training data coverage,a fuzzy modeling method that incorporates empirical knowledge in the form of TSK(Takagi-Sugeno-Kang) fuzzy rules is proposed.In the structure identification process,a fuzzy rule fusion method is proposed to determine the initial fuzzy rules.In the parameter identification process,the original objective function of the gradient descent method is improved and the evaluating parameter of the accuracy of empirical knowledge is introduced to trade off the influence of sample data and empirical knowledge.The numerical simulation and engineering case studies show that the proposed method can offer more reliable and accurate forecasting values.更多还原