This system has the faculty that the self-study capability of the neural network and fuzzy theory can process uncertain information. The simulation results show the system is highly effective in conducting the fuzzy quantitative treatment of sample modes of machinery, dramatically improving the convergence of a neural network training. By many tests of fuzzy neural network system, when real data is close to sample data, the system can reach the accurate forecasting result.On the other hand, when real data deviates sample data, the system also can reach he accurate forecasting result. The system can satisfy the forecasting requirement of machinery surplus life span.%该系统具备了神经网络自学习和模糊推理处理不确定信息的能力。通过仿真实验和仿真结果表明，该系统能有效地进行农业设备样本模式的模糊量化处理，极大地改善了神经网络训练的收敛性。通过对模糊神经网络系统的多次测试，当实际数据与学习样本接近时，该系统能够得出与样本相同的预测结果；当输入数据与学习样本偏离时，该系统依然能够得出准确的预测结果，达到了农业设备剩余寿命预测的要求。
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