摘要： 针对现有人工神经网络学习新知识会破坏已获得知识的问题，根据生物智能扩展的思想，提出一种粒计算模糊增殖神经场学习方法，将粒计算商空间理论和人工神经场理论有机结合，并引入自治神经网络中，采用分治方法和嵌入机理，把大任务分成小任务，实现知识积累、继承和不断完善。实验结果证明了该方法的合理性和可行性。

关键词: 粒计算, 模糊计算, 知识增殖, 神经场

Abstract: In view of the problem that the existed artificial neural network will destroy the obtained knowledge when it studies new knowledge, according to expansion idea of the biological intelligence, a learning method of Fuzzy Incremental Neural Field Based-on Granular Computing(FINFGC) is proposed. This method organically combines the character of the quotient space with the theory of neural field and introduces in the autonomous neural network, makes use of the partitioning method and the inserting mechanism, divides the big task into the small task so as to realize the knowledge accumulation, inheritance and perfection unceasingly. Experimental results show this method is rational and feasible.

Key words: granular computing, fuzzy computing, knowledge increment, neural field

中图分类号: 

• N945