Also, the contour plot of the same

Also, the contour plot of the same is depicted in the Fig. 17.The developed ANFIS model structure with 2 input neurons & 1 output neuron along with 4 hidden layers (input membership function, rule base, membership function, and aggregated output) are shown in the Fig. 18. The training of the neural network by using the fuzzy rule base for the selection of the proper & optimal rule is taken care of by the designed ANFIS controller. Note that 7 by 7 rules are used in the hidden layers. The neuron 1 is connected to 7 fuzzy rules & the neuron 2 is also connected to the 7 fuzzy rules. The hidden layers contains 49-49 neurons to deal the problem (for selection of the proper rule base, because the rule base are written randomly in fuzzy, the neural network selects the right optimal rule base to fire). The 2 input neurons, viz., the error, change in error is given as input to the 1st hidden layer of the ANN as shown in the Fig. 18. This 1st hidden layer deals with various input membership functions. In the 2nd & 3rd hidden layer, the set of 49 fuzzy rules are properly identified by training & the set of optimal rules are selected. These set of optimum rules are available at the 4th hidden layer. Out of the 49 rules, the optimal rules are fired here & the de-fuzzified output is obtained as the output neuron. The de-fuzzified output is further used to generate the firing pulse to be applied to the inverter bridge,which is further used to control the speed of the IM drive.
Fig. 6 : FIS editor with 1 input
Fig. 7 : FIS editor with 2 inputs & 1 output ; Importing of the .fis file from the source
Fig. 8 : Membership function editor

結果 (中文) 1:

Also, the contour plot of the same is depicted in the Fig. 17.The developed ANFIS model structure with 2 input neurons & 1 output neuron along with 4 hidden layers (input membership function, rule base, membership function, and aggregated output) are shown in the Fig. 18. The training of the neural network by using the fuzzy rule base for the selection of the proper & optimal rule is taken care of by the designed ANFIS controller. Note that 7 by 7 rules are used in the hidden layers. The neuron 1 is connected to 7 fuzzy rules & the neuron 2 is also connected to the 7 fuzzy rules. The hidden layers contains 49-49 neurons to deal the problem (for selection of the proper rule base, because the rule base are written randomly in fuzzy, the neural network selects the right optimal rule base to fire). The 2 input neurons, viz., the error, change in error is given as input to the 1st hidden layer of the ANN as shown in the Fig. 18. This 1st hidden layer deals with various input membership functions. In the 2nd & 3rd hidden layer, the set of 49 fuzzy rules are properly identified by training & the set of optimal rules are selected. These set of optimum rules are available at the 4th hidden layer. Out of the 49 rules, the optimal rules are fired here & the de-fuzzified output is obtained as the output neuron. The de-fuzzified output is further used to generate the firing pulse to be applied to the inverter bridge,which is further used to control the speed of the IM drive.图 6： 1 输入 FIS 编辑器图 7： FIS 编辑器与 2 个输入和 1 个输出 ；来自源的.fis 文件导入图 8： 隶属函数编辑器

結果 (中文) 3:

同时，相同的轮廓图，在图17所示。ANFIS模型结构2输入1输出神经元的神经元&连同4个隐藏层（输入隶属函数，规则库，隶属函数，并汇总输出）在图18所示。利用模糊规则库的正确&最优规则的选择神经网络的训练是由设计ANFIS控制器。注意7 7规则用于隐藏层。神经元1连接到7的模糊规则&神经元2也连接到7个模糊规则。隐层神经元包含49-49处理问题（在适当的规则库，规则库的选择因为写随机模糊神经网络，选择合适的优化规则库火灾）。2个输入神经元，即。，误差，误差变化作为输入到第一个隐藏层的神经网络如图18所示。这第一个隐藏层处理各种输入隶属函数。在第二&第三隐藏层，49个模糊规则集的训练&最优规则集的正确识别选择。这套优化规则可在第四个隐藏层。出了49条规则，最优的规则在这里发射&去模糊化，得到输出作为输出神经元。去模糊化的输出是用来产生触发脉冲被应用到逆变桥，这是用于控制IM驱动器的速度。
图6：FIS编辑器1输入
图7：FIS编辑器2输入1输出&；进口的。从源
图8 FIS文件：隶属函数编辑器