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中文摘要：
BP神经网络具有优良的非线性映射能力，可以很好地描述频率特征和诊断结果之间的关系。针对BP神经网络存在局部极小值和收敛速度慢等问题，提出了一种基于Gauss-Newton的改进的BP网络。论述了Gauss-Newton神经网络的基本原理以及学习、运行过程，通过模拟运算指出了Gauss-Newton神经网络具有较快的学习速度，进而探讨了Gauss-Newton神经网络在旋转机械故障诊断中的应用，将该网络模型应用于旋转机械故障诊断，显示出Gauss—Newton网络具有诊断精度高、容错性和稳定性好的优势。
英文摘要：
BP Neural network is effective for dealing with non-linear mapping which could satisfaetorily describe the non-linear relations between frequency character and diagnosis results. A kind of BP neural network based on Gauss-Newton Baekpropagation optimization algorithm is introudueed in detail to eomeover the disadvantages of standard BP algorithm. This paper discusses the Gauss-Newton neural networks and the basic principles of learning and operation process through simulation. It is proved that learning speed of Gauss-Newton neural network is fast. The application of the Gauss-Newton in rotating machinery fault diagnosis is also studied. The experiment shows that the Gauss-Newton network has great advantage in diagnosis accuracy, fault tolerance and stability.
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