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Estimating the Number of Hidden Nodes of the Single-Hidden-Layer Feedforward Neural Networks
2019 15th International Conference on Computational Intelligence and Security (CIS), 2019In order to solve the problem that there is no effective means to find the optimal number of hidden nodes of single-hidden-layer feedforward neural network, in this paper, a method will be introduced to solve it effectively by using singular value decomposition.
Guang-Wei Cai, Zhi Fang, Yue-Feng Chen
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Modular expansion of the hidden layer in Single Layer Feedforward neural Networks
2016 International Joint Conference on Neural Networks (IJCNN), 2016We present a neural network architecture and a training algorithm designed to enable very rapid training, and that requires low computational processing power, memory and time. The algorithm is based on a modular architecture, which expands the output weights layer constructively, so that the final network can be visualised as a Single Layer ...
Migel D. Tissera, Mark D. McDonnell
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Neural Networks with Marginalized Corrupted Hidden Layer
2015Overfitting is an important problem in neural networks (NNs) training. When the number of samples in the training set is limited, explicitly extending the training set with artificially generated samples is an effective solution. However, this method has the problem of high computational costs.
Yanjun Li, Xin Xin 0001, Ping Guo 0002
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[Exploration of the hidden layers of genome].
Yi chuan = Hereditas, 2006In recent years, geneticists have been exploring the less visible parts of genome more thoroughly. They are coming to realize that these much more layers of genomic information, distinct from the protein-coding genes, connect in surprisingly deep and potent ways to growth and development in the high eukaryotes.
Geng-Sheng, Cao, Ai-Lian, Liu, Ning, Li
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Feedback Stabilization Using Two-Hidden-Layer Nets
1991 American Control Conference, 1991The representational capabilities of one-hidden-layer and two-hidden-layer nets consisting of feedforward interconnections of linear threshold units are compared. It is remarked that for certain problems two hidden layers are required, contrary to what might be in principle expected from the known approximation theorems.
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