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, im proved 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 a-mount of complexity in calculating and is more effective. Finally, the training algorithm of fuzzy neural network is im proved, and it can be applied in weather forecast and security situation prediction. Test results show that this method significantly improves the effectiveness of training,reduces the order of training, time complexity and training error.%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.
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