When the initial parameters of the fuzzy cerebellar model neural network (FCMNN) are not properly selected, there is a great possibility that the error function converges to the local minimum region or diverges under the gradient descent back propagation (BP) algorithm, which will affect the classification ability of FCMNN. Aiming at this problem, GA is used to optimize the initial center positions and width of the activation function and weight of FCMNN. After obtaining the optimal initial parameters, the internal parameters of FCMNN can approach the convergence value of minimum error more quickly and accurately through further training of the network, so as to obtain better network learning performance. The introduction of GA to optimize the initial value of FCMNN can effectively reduce the blindness and time cost of manual selection of initial parameters, and further improve the intelligence of neural network diagnosers. The simulation and experimental results show that the classification ability of the GA-FCMNN and GA can effectively find the optimal combination in the set data domain.

如果没有正确选择模糊小脑模型神经网络（FCMNN）的初始参数，则误差函数很有可能会收敛到局部最小区域或在梯度下降反向传播（BP）算法下发散，这会影响FCMNN的分类能力。针对此问题，GA用于优化初始中心位置，激活函数的宽度和FCMNN的权重。在获得最优的初始参数后，通过对网络进行进一步的训练，FCMNN的内部参数可以更快，更准确地接近最小误差的收敛值，从而获得更好的网络学习性能。引入遗传算法来优化FCMNN的初始值可以有效减少手动选择初始参数的盲目性和时间成本，并进一步提高神经网络诊断仪的智能性。仿真和实验结果表明，GA-FCMNN和GA的分类能力可以有效地在集合数据域中找到最优组合。