基于神经网络的移动机器人的故障诊断方法研究(15)
S8 0.8756 0.0089 0.1012 0.9325 0.6081 0.7953 0.0223 0.6706
S9 0.0529 0.3777 0.9629 0.5594 0.4593 0.2520 0.9759 0.6655
S10 0.9909 0.6106 0.2391 0.3441 0.5666 0.3812 0.7469 0.1097
输出数据
Output1 Fault-10 0.9377 0.0508 0.9016 0.2509 0.9694 0.4314 0.1009 0.9868
Fault-11 0.2505 0.6491 0.1722 0.1333 0.0827 0.5833 0.8308 0.1487
Fault-12 0.4715 0.7747 0.2582 0.9486 0.0吹冰 0.8015 0.3964 0.2257
Fault-13 0.4430 0.8340 0.0017 0.2715 0.5863 0.9505 0.4536 0.3218
Output2 Fault-20 0.3641 1.0300 0.2844 0.9003 0.9770 0.2587 0.9041 0.8009
Fault-21 0.1082 0.2374 0.8610 0.0815 0.0761 0.8092 0.5685 0.9130
Fault-22 0.9624 0.0307 0.3742 0.2399 0.2898 0.7358 0.0748 0.1482
Fault-23 0.0055 0.0008 0.7640 0.4556 0.3061 0.9905 0.3007 0.8243
Output3 Fault-30 0.8801 0.2274 0.9051 0.8935 1.0663 0.8245 0.9768 0.2387
Fault-31 0.0145 0.9503 0.1169 0.2202 0.0168 0.1418 0.1094 0.1949
Fault-32 0.1204 0.2126 0.0868 0.5742 0.0833 0.2387 0.6051 0.9312
Fault-33 0.0851 0.3348 0.1912 0.2655 0.3329 0.1050 0.2725 0.1750
结果
results Fault-10 Fault-13 Fault-10 Fault-12 Fault-10 Fault-13 Fault-11 Fault-10
Fault-22 Fault-20 Fault-21 Fault-20 Fault-20 Fault-23 Fault-20 Fault-21
Fault-30 Fault-31 Fault-30 Fault-30 Fault-30 Fault-30 Fault-30 Fault-32