Optimum steepest descent higher level learning radial basis function network
作者：
Highlights：
• We model a neural network with adaptive structure and dynamic learning.
• Higher level learning components helps the network to think before learning.
• Number of hidden neurons are decreased and redundant samples are removed.
• Increases classification accuracy, reduces detection time & architecture complexity.
• Various abnormality levels in vital parameters of multiple patients are classified.
摘要
•We model a neural network with adaptive structure and dynamic learning.•Higher level learning components helps the network to think before learning.•Number of hidden neurons are decreased and redundant samples are removed.•Increases classification accuracy, reduces detection time & architecture complexity.•Various abnormality levels in vital parameters of multiple patients are classified.
论文关键词：Neural network,Radial basis function,Dynamic learning,Optimum steepest descent,Higher level components,Healthcare
论文评审过程：Available online 6 July 2015, Version of Record 10 July 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.06.036
