结果表明当目标误差为0．01，径向基函数的分布常数为4时，网络达到最优化，总的正确识别率为96％。

When error goal is 0. 01 and speed constant of radial basis function is 4, the network achieves optimization, and the total correct rate is 96%.

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当目标误差为0．01，径向基函数的分布常数为5时，网络达到最优化，总的正确识别率为98％。

When the error goal is 0. 01 and speed constant of radial basis function is 5, and the network achieves the optimization, and the total correct rate is 98%.

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利用时变基频的求解方法，建立基于径向基函数网络的时变基频识别模型，解决“深度”复杂性问题；

The identifying model of time variation was built, on the basis of radial basis function neural network to solve the problem of diagnosis depth.

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