计算机科学 ›› 2011, Vol. 38 ›› Issue (11): 196-199.

• 人工智能 • 上一篇    下一篇

一种基于改进T-S模糊推理的模糊神经网络学习算法

许哲万,李昌皎,王爱侠,郭先日   

  1. (东北大学信息科学与工程学院 沈阳110819)(金日成综合大学计算机科学大学 平壤)
  • 出版日期:2018-12-01 发布日期:2018-12-01

Training Algorithm of Fuzzy Neural Network Based on Improved T-S Fuzzy Reasoning

  • Online:2018-12-01 Published:2018-12-01

摘要: 针对模糊神经网络学习算法计算量过大,在预测模型设计中提出了基于改进T-S模糊推理的模糊神经网络学习算法。主要工作如下:首先,改进T-S模糊推理方法,定义基于偏移率的T-s模糊推理方法;然后,通过将此模糊推理方法与基于合成规则的模糊推理方法及距离型模糊推理方法相比较可以看出,所提方法有较少的计算量,且比较有效;最后,在此基础上改善了模糊神经网络学习算法,并将其应用于天气预测与安全态势预测。测试结果表明,该方法明显改善了学习效率,减少了预测模型设计中的学习次数与时间复杂度,并降低了学习误差。

关键词: 模糊神经网络,模糊推理,天气预测,安全态势

Abstract: A training algorithm of fuzzy neural network based on improved T-S fuzzy reasoning was proposed in the predicate model design, in order to reduce the complexities of the algorithm. The main work is as below. Firstly, improved T-S fuzzy reasoning method based on moving rate is defined. Then, compared with existing fuzzy reasoning method based on composed rules and distance-type fuzzy reasoning method,new fuzzy reasoning algorithm has a less amount of complexity in calculating and is more effective. Finally, the training algorithm of fuzzy neural network is improved,and it can be applied in weather forecast and security situation prediction. Lest results show that this method significantly improves the effectiveness of training,reduces the order of training,time complexity and training error.

Key words: Fuzzy neural network,Fuzzy reasoning,Weather forecast,Security situation

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