Hybrid approach for fuzzy system design

A hybrid approach for fuzzy system design based on clustering and a kind of neurofuzzy networks is proposed. An unsupervised clustering technique is firstly used to determine the number of if-then fuzzy rules and generate an initial fuzzy rule base from the given input-output data. Then, a class of neurofuzzy networks is constructed and its weights are tuned so that the obtained fuzzy rule base has a high accuracy. Finally, two examples of function approximation problems are given to illustrate the effectiveness of the proposed approach.

一种基于划分聚类和模糊神经网络的机器学习方法

将基于划分的模糊聚类算法和一般模糊极小极大神经网络分类算法相结合,提出了一种新的机器学习方法,实现了基于类比的案例推理学习模型。具体实现思想是,首先利用基于确定性退火技术的划分聚类算法对已知案例进行聚类标识,由所得结果建立一般模糊极小极大神经网络分类模型,然后用该模型实现新目标问题的案例相似性检索,最后针对目标问题结果案例完成案例学习。通过实例表明,该算法具有较好性能,并在基于案例推理的固体火箭发动机总体设计中成功应用,得到了论域覆盖面大的设计结果集。