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具有正态模糊参数的模糊神经元网络学习机制
A Learning Algorithm of the Neural Network with Bell-Shaped Fuzzy Parameters
【摘要】 描述一个多层前馈式模糊神经元网络的学习机制.首先给出网络结构,其连接权值和阀值均为正态模糊数;然后采用模糊数学中区间数运算规则,对传统的BP算法进行了扩展,提出多层前馈式模糊神经元网络的基于正态模糊数的学习算法,用该算法训练后神经元网络能够完成确定或不确定的信息输入到近似结果输出的非线形映射;最后通过对模糊推理规则的学习考查分析了网络及其学习算法的性能
【Abstract】 Described a learning algorithm of a multilayer feedforword fuzzy nerual network.First we gve the network architecture whose weights and thresholds are given as bell-shaped fuzzy numbers.Second we adopted the intervals algorithm of the fuzzy sets,extend the traditional BP-algorithm and proposed a bell-shaped fuzzy number based learning algorithm of a multilayer feedforword fuzzy neural network.After training with this proposed algorithm,the fuzzy neural network can perform the non-line ampping between definite or indefinite inputs vectors and crisp outputs.Last by learning the fuzzy reasoning rules we examine and analyze the ability of the proposed fuzzy neural network and its learning algorithm.
【Key words】 fuzzy neural network; BP-algorithm; intervals; fuzzy inference;
- 【文献出处】 云南大学学报(自然科学版) ,JOURNAL OF YUNNAN UNIVERSITY (NATURAL SCIENCES) , 编辑部邮箱 ,1997年S2期
- 【分类号】TP301.6
- 【被引频次】2
- 【下载频次】55