BP neural networks and GA were applied to the optimal design of mechanical structure.
将BP神经网络和遗传算法相结合运用于快速设计的结构优化问题。
The FNN constituted by NGA, whose convergence rate, optimizing capability and distinguish effect are superior to those of GA and BP, is accorded with project need of discriminating.
以NGA训练的模糊网络模型,在收敛速度、寻优能力和辨别效果方面均优于BP、GA算法下的模型,符合缺陷识别的工程需要。
Simulation results of practical example show that the method can improve the calculation accuracy and the speed of the convergence process compared with BP and BP trained by GA.
实例计算表明,与BP算法及BP与GA结合算法比较,该方法在提高误差精度的同时可以加快训练收敛的速度。
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