【摘要】 提出了一种基于熵聚类的模糊神经网络味觉信号识别系统模型 ,该模型利用聚类方法实现模糊输入空间划分和模糊IF THEN规则提取 ,并使用梯度下降法对系统参数进行精炼 系统兼具有良好的可解释性和学习能力 ,对 1 1种矿泉水味觉信号的识别实验结果表明了该系统的可行性和有效性更多还原

【Abstract】 A fuzzy neural network for identifying 11 kinds of mineral waters is developed based on an entropy based clustering method Partitioning fuzzy input space and extracting fuzzy IF THEN rules are implemented employing the clustering method and the Gradient Descent algorithm is used for optimizing system parameters, so that the system has good interpretability and learning capability Experimental results show that the system is feasible and effective for identifying 11 kinds of mineral waters by its taste signals更多还原