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A Genetic Fuzzy System Based On Improved Fuzzy Functions

机译：一种基于改进模糊功能的基因模糊系统

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—Fuzzy inference systems based on fuzzy rule bases (FRBs) have been successfully used to model real problems. Some of the limitations exhibited by these traditional fuzzy inference systems are that there is an abundance of fuzzy operations and operators that an expert should identify. In this paper we present an alternate learning and reasoning schema, which use fuzzy functions instead of if…then rule base structures. The new fuzzy function approach optimized with genetic algorithms is proposed to replace the fuzzy operators and operations of FRBs and improve accuracy of the fuzzy models. The structure identification of the new approach is based on a supervised hybrid fuzzy clustering, entitled Improved Fuzzy Clustering (IFC) method, which yields improved membership values. The merit of the proposed fuzzy functions method is that the uncertain information on natural grouping of data samples, i.e., membership values, is utilized as additional predictors while structuring fuzzy functions and optimized with evolutionary methods. The comparative experiments using real manufacturing and financial datasets demonstrate that the proposed method is comparable or better in modeling systems of regression problem domains.
机译：-Fuzzy推理系统基于模糊规则基础（FRB）已经成功地用于模拟真正的问题。这些传统模糊推理系统呈现的一些局限性是专家应该识别的丰富的模糊操作和运营商。在本文中，我们介绍了一个替代的学习和推理模式，它使用模糊函数而不是IF ...然后规则基础结构。提出了用遗传算法优化的新模糊功能方法，以取代FRB的模糊操作员和操作，提高模糊模型的精度。新方法的结构识别是基于受监督的混合模糊聚类，题为改进的模糊聚类（IFC）方法，其产生了改进的隶属值。所提出的模糊函数方法的优点是数据样本的自然分组的不确定信息，即隶属值，作为额外的预测因子，同时构建模糊功能并用进化方法进行优化。使用真实制造和金融数据集的比较实验表明，在回归问题域的建模系统中，所提出的方法是可比的或更好的。

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