基于区间值模糊逻辑神经元的区间值模糊C-均值聚类神经网络

本文提出了一种基于区间值模糊逻辑神经元的三层前馈自组织学网络模型，用来实现区间值模糊Ｃ－均值聚类分析，网络第一，二层神经元的输入，输出和权连续取值属于区间值模糊集Ｉ（０，１）第一层神经元为区间值线性神经元；第二层为区间值模糊相等神经元，其功能是实现输入样本与各类的匹配运算，本文采用区间值模糊相等关系作为匹配的指标，为了定义区间值模糊相等神经元，本文在点值模糊相等关系的基础上推导了区间值模糊相等关系

基于区间值模糊逻辑神经元的区间值模糊C-均值聚类神经网络

In this paper a three-layered feedforward self-organizing neural network model is proposed based on interval-valued fuzzy logic neurons in order to realize the interval-t,alued fuzzy C-means (IVFCM) clustering analysis. The inputs/outputs of the first and second layer neurons and weights between them belong to foe interval-valued fuzzy sets I 0, 1]. The neurons in the first layer are intervalvalued linear neurons; The neurons in the second layer are interval-valued fuzzy equality neurons (IVFEN) which realize the matching computation between the input samples and all of the clusters. Interval-valued fuzzy equality relation is used to compute matching. In order to define the model of IVFEN, the computation method of interval-valued fuzzy equality relation is deduced based on point-valued fuzzy equality relation. The neurons in the third layer are fuzzy competition neurons. The outputs of the third-layer neurons represent the fuzzy classification result of the input sample. Furthermore, intervalvalued fuzzy competition learning (IVFCL) is proposed to train the IVFCM neural network (IVFCMNN)