A comprehensively improved BP neural network is adopted, and an improvement for momentum adaptive adjustment is carried out.
神经网络采用一种综合改进的BP网络，并进行了动量项自适应调整的改进。
At last, the total scheme was validated experimentally. The experiment proves that the method of the dynamic range adaptive adjustment is feasible, and the imaging quality is improved evidently.
最后，对整体方案进行了实验验证，通过实验证明了自适应动态范围调整方法的可行性，并获得了较好的效果，成像质量有明显提高。
The improved BP algorithms based on adaptive parameters adjustment and error contracting gradually are presented, which are applied successfully to fault diagnosis of steam- turbine generator unit.
提出了自适应学习率及动量因子的BP神经网络算法和误差逼近度渐近收缩学习的BP神经网络算法，并将其应用于汽轮发电机组振动故障诊断与识别。
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