Layer 3 : This layer is called as t

Layer 3 : This layer is called as the rule layer. Each node (each neuron) in this layer performs the pre-condition matching of the fuzzy rules, i.e., they compute the activation level of each rule, the number of layers being equal to the number of fuzzy rules. Each node of these layers calculates the weights which are normalized.

Layer 4 : This layer is called as the defuzzification layer & provides the output values y resulting from the inference of rules. Connections between the layers l3 & l4 are weighted by the fuzzy singletons that represent another set of parameters for the neuro fuzzy network.
Layer 5 : This layer is called as the output layer which sums up all the inputs coming from the layer 4 and transforms the fuzzy classification results into a crisp (binary).The ANFIS structure is tuned automatically by least-squareestimation & the back propagation algorithm. The algorithm shown above is used in the next section to develop the ANFIS controller to control the various parameters of the induction motor. Because of its flexibility, the ANFIS strategy can be used for a wide range of control applications.

結果 (中文) 1:

第三层： 这一层被称为规则层。在这一层的每个节点 （每个神经元） 执行前置条件匹配的模糊规则，即，他们计算的每个规则，等于模糊规则数目的层数的激活水平。这些层的每个节点计算归一化的砝码。第 4 层： 这一层被称为反模糊化层及提供的推理规则所引起的输出值 y。层 l3 和 l4 之间的连接被加权的模糊的单身人士表示另一个组的模糊神经网络的参数。层 5： 这一层被称为总结来自 4 层的所有输入，并将模糊分类结果转换为脆 （二进制） 的输出层。ANFIS 结构是由至少 squareestimation 和反向传播算法自动进行调整。如上所示的算法在下一节中用于开发自适应神经模糊控制器来控制的感应电机的各种参数。因为它的灵活性，ANFIS 策略可用于控制应用程序的种类繁多。

結果 (中文) 3:

3层：此层为准则层。每个节点（神经元）在这层执行的先决条件的模糊规则，即匹配，他们计算出每个规则的激活水平，层等于模糊规则数。这些层的每个节点计算其归一化权重

层4：这层被称为模糊化层&提供输出值Y从规则的推理导致。层L3&L4之间的连接是由表示为模糊神经网络的参数，另一组模糊单身
层5加权：这层被称为输出层，总结所有的输入来自4层和转换的模糊分类结果为清晰（二进制）。ANFIS结构通过最小二乘法&反向传播算法自动调整。上面的算法应用在下一节中建立ANFIS控制器控制感应电机的各种参数。由于其灵活性，ANFIS策略可用于范围广泛的控制应用。