Extracting rules from fuzzy neural network by particle swarm optimisation
Extracting rules from fuzzy neural network by particle swarm optimisation
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DOI：
10.1109/icec.1998.699325
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发表时间：
1998-05
期刊：
影响因子：
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通讯作者：
Zhenya He;Chengjian Wei;Yang Luxi;Gao Xiqi;Yao Susu;R. Eberhart;Yuhui Shi
中科院分区：
文献类型：
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作者：
Zhenya He;Chengjian Wei;Yang Luxi;Gao Xiqi;Yao Susu;R. Eberhart;Yuhui Shi
提出了一种四层模糊神经网络实现输入输出样本的知识获取。网络参数包括必要的隶属函数的输入变量和结果参数的调整和识别使用一种改进的粒子群算法，它使用每个粒子的最佳当前性能的邻居，以取代最好的前一个，并使用非累积的变化率，以取代的累积的搜索过程。然后对训练好的网络进行修剪，以便提取和解释一般规则。实验结果表明，与其他模糊神经网络方法相比，该方法可以获得相似的分类规则。
A four layer fuzzy neural network is presented to realise knowledge acquisition from input-output samples. The network parameters including the necessary membership functions of the input variables and the consequent parameters are tuned and identified using a modified particle swarm algorithm which uses each particle's best current performance of its neighbours to replace the best previous one and uses a non accumulative rate of change to replace the accumulative one for accelerating search procedure. The trained network is then pruned so that the general rules can be extracted and explained. The experimental results have shown that the similar classification rules can be obtained in comparison to that of other fuzzy neural approaches.