Xing Deng1,2, Haijian Shao1,2,*
Energy Engineering, Vol.117, No.5, pp. 279-287, 2020, DOI:10.32604/EE.2020.011619
Abstract Recurrent neural networks (RNNs) as one of the representative deep
learning methods, has restricted its generalization ability because of its indigestion
hidden-layer information presentation. In order to properly handle of hidden-layer
information, directly reduce the risk of over-fitting caused by too many neuron
nodes, as well as realize the goal of streamlining the number of hidden layer neurons, and then improve the generalization ability of RNNs, the hidden-layer information of RNNs is precisely analyzed by using the unsupervised clustering
methods, such as Kmeans, Kmeans++ and Iterative self-organizing data analysis
(Isodata), to divide the similarity of raw data points, and maps… More >
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