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。

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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。

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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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