Global Stability of Complex-Valued Recurrent Neural Networks With Time-Delays

By Jin Hu, Jun Wang. IEEE Transactions on Neural Networks and Learning Systems, 2012

351 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/7ae75c1d89fb530f8f2cd2d995da8edc28df6fe2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Yoshua Bengio
2. Derong Liu
3. Huaguang Zhang
4. Mirco Ravanelli
5. Zhanshan Wang

In Electrical and Electronic Engineering

1. Derong Liu
2. Huaguang Zhang
3. Yoshua Bengio
4. Mirco Ravanelli
5. Zhanshan Wang
6. Qinglai Wei
7. Frank L. Lewis
8. Hamidreza Modares

In Statistical and Nonlinear Physics

1. Mirco Ravanelli
2. Yoshua Bengio
3. Derong Liu
4. Huaguang Zhang
5. Zhanshan Wang
6. Kyunghyun Cho
7. Li Deng
8. Hugo Larochelle

Citations in the corpus

• A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks (2014, background): “Due to the different transmission channels and media, time delays are unavoidable in real systems [56], [110], [176], [228].”
• Quaternion Recurrent Neural Networks (2018, background): “The QRNN is an extension of the real-valued (Medsker & Jain, 2001) and complex-valued (Hu & Wang, 2012; Song & Yam, 1998) recurrent neural networks to hypercomplex numbers.”