SOM neural network classification--diesel engine f
2016-08-23
0 0 0
no vote
Other
Earn points
Background of SOM neural network:
Self-organizing feature map neural networks (SOM) simulation of self-organizing feature mapping of the brain function, is an unsupervised competitive learning feedforward neural networks, without oversight from the organizational learning in training. It by learning important features in a set of data can be extracted or some kind of internal rules are categorized by discrete-time approach. Networks can be mapped to any higher dimensional input low-dimensional space, enter data and makes some similar properties to geometry within the adjacent feature on maps. Thus, in mapping the output layer into a one-dimensional or two-dimensional discrete graphics, and keeping the same topology. This classification reflects the essential difference between sample sets, greatly weakening the consistency criterion in the human factor. [The following detailed theoretical knowledge of SOM network..........]
Diesel engine fault diagnosis background
Self-organizing feature map neural networks (SOM) simulation of self-organizing feature mapping of the brain function, is an unsupervised competitive learning feedforward neural networks, without oversight from the organizational learning in training. It by learning important features in a set of data can be extracted or some kind of internal rules are categorized by discrete-time approach. Networks can be mapped to any higher dimensional input low-dimensional space, enter data and makes some similar properties to geometry within the adjacent feature on maps. Thus, in mapping the output layer into a one-dimensional or two-dimensional discrete graphics, and keeping the same topology. This classification reflects the essential difference between sample sets, greatly weakening the consistency criterion in the human factor. [The following detailed theoretical knowledge of SOM network..........]
Diesel engine fault diagnosis background
matlab
分类
神经网络
SOM
数据
故障
诊断
柴油机
Related Source Codes
GMSK Linear Receiver
0
0
no vote
NSGA-II algorithm
0
0
no vote
NSGA-III multi-objective optimization algorithm
0
0
no vote
Compressed sensing example
0
0
no vote
CFAR detector example
0
0
no vote
No comment