摘要
传统的信息神经网络没有考虑特征分量的故障信息重要度,使得识别效率大为降低。针对上述问题,文章提出了一种改进型信息神经网络,充分考虑各特征分量的重要度权值,优化了网络结构,提出了结构参数的优化算法;最后,将改进型信息神经网络应用于模式识别和故障诊断,结果证明了该方法的有效性。
The fault information importance of feature vector isn＇t taken into account in the Traditional Information Neural Network（INN）,so its recognition efficiency decreases sharply.Aiming at above problems,an improved INN is proposed in this paper considering importance weight value,which optimizes network structure and presents optimization arithmetic of structure parameters.Finally,the improved INN is applied to the pattern recognition and fault diagnosis and its availability is shown by results.
出处
《仪表技术》
2010年第5期57-59,共3页
Instrumentation Technology
基金
国家自然科学基金资助项目(50175109)
关键词
信息神经网络
模式识别
故障诊断
information neural network
pattern recognition
fault diagnosis
