NDLI: A Method of Adaptive Neuron Model (AUILS) and Its Application

Content Provider	IEEE Xplore Digital Library
Author	Zhai Jun Yang Xiao-Jia Chen Yan
Copyright Year	2006
Description	Author affiliation: Sch. of Econ. & Manage., Dalian Maritime Univ. (Zhai Jun; Yang Xiao-Jia; Chen Yan)
Abstract	This paper presents an adaptive neuron model utilizing information of local samples - the AUILS neuron model. Differing from traditional neuron models, the AUILS neuron model fully employs the experience samples information within the local range and well embodies the association and analogy functions of cerebrum. The neural network, which is simple in structure and fast in learning speed, can realize the nonlinear mapping relationship between multi-input and multi-output. Through investigating the properties of AUILS and learning algorithm based on gradient, we build a method based on the neuron model for rotary machine fault diagnosis, which takes full advantage of expert experiences to estimate the reliability of fault existing according to the vibration signals of rotary machine in operation. It is verified that using the AUILS model, expert experiences can be well expressed in the diagnosis results
Starting Page	47
Ending Page	52
File Size	343543
Page Count	6
File Format	PDF
ISBN	1424404754
DOI	10.1109/COGINF.2006.365675
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Neural networks Fault diagnosis Artificial neural networks Machine learning Knowledge representation Feedforward neural networks Management training State estimation Personnel Rotary Machine Neuron Model Fault Diagnostics
Content Type	Text
Resource Type	Article

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