摘要： 结合输入空间的聚类特性和输出空间实时逼近特性，在模糊聚类的目标函数中引入恰当的反馈因素，基于自适应动态目标函数，该文提出一种新的模糊聚类神经网络实现算法。该算法在收敛稳定性、收敛速度、初值敏感性方面，相对于传统模糊聚类算法有了明显改善，相关实验表明，该算法具备高效、稳定的工程应用价值。

关键词: 模糊聚类, 神经网络, 目标函数, 自适应, 动态, 选址决策

Abstract: This paper proposes a novel adaptive dynamic objective function of fuzzy clustering algorithm, the new objective function integrates the clustering characteristic of input space and the real time approximate characteristic of output space, so importing felicitous adaptive feedback factors into the objective function. Extraordinary neural network to implement the fuzzy clustering algorithm is proposed. The new algorithm has better performance in stable convergence rate, convergence speed, and threshold sensitivity compared with traditional fuzzy clustering algorithm. Experiments show the algorithm provides more efficient and more stable application worthiness.

Key words: fuzzy clustering, neural network, objective function, adaptive, dynamic, address selection strategy

中图分类号: 

• TP183