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针对航空电机发热及散热受电机功率、结构,以及由于海拔高度改变带来的大气温度、粘度、压力变化等众多因素影响,温升模型难以准确建立的问题,通过已有试验数据,建立起遗传算法-神经网络表面温升模型,解决了遗传算法局部搜索能力差的问题,降低了神经网络陷入局部最小点的可能性。试验表明,该模型实现了对航空电机表面温升的智能预测。
In view of the fact that the heating and cooling of aero-electric motor are affected by many factors such as the power and structure of the motor and the atmospheric temperature, viscosity and pressure caused by the altitude change, it is difficult to accurately establish the temperature rise model. Based on the existing experimental data, Algorithm - neural network surface temperature rise model, to solve the problem of local search ability of genetic algorithm is poor, reducing the possibility of neural network into a local minimum. Experiments show that the model achieves intelligent prediction of the surface temperature of aeronautic motor.