基于优化步长和梯度法的置信规则库参数学习方法  

An Algorithm for Training Parameters in Belief Rule-bases Based on Gradient Methods with Optimization Step Size

作　　者：常瑞[1] 张速[1] 

机构地区：[1]华北水利水电学院,河南郑州450011

出　　处：《华北水利水电学院学报》2011年第1期154-157,共4页North China Institute of Water Conservancy and Hydroelectric Power

摘　　要：置信规则库是传统规则库的推广.置信规则库中的参数由专家根据经验人为给定,削弱了置信规则库系统的仿真能力,因此,基于优化步长和梯度法设计了一种新的算法以实现规则库参数的自学习能力.采用该算法对一个已经建立的置信规则库参数进行了训练,训练结果表明新的算法具有收敛速度快、精度高等优点.Belief rule base is an extension of traditional rule base. The parameters in belief rule base are provided by experts subjectively, which weaken the simulation ability of the system. Based on the gradient methods with optimization step size, a new simple optimization algorithm is proposed to realize the self-learning capability of the rule base parameters, The algorithm has been applied to train parameters in an existing belief rule-base. The training results show that the new algorithm is simple, fast and effective.

关 键 词：置信规则库 学习模型 非线性规划 梯度法 优化步长 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]