论文信息 - Dynamic recurrent neural networks: Theory and applications

Dynamic recurrent neural networks: Theory and applications

This special issue illustrates both the scientific trends of the early work in recurrent neural networks, and the mathematics of training when at least some recurrent terms of the network derivatives can be non-zero. Herein is a brief description of each of the papers. We have organized this description into two parts. The first part contains the papers that are mainly theoretical, and the second part contains the papers that are mainly applications. The order of papers is alphabetical by first author.

[1] S C Kleene,et al. Representation of Events in Nerve Nets and Finite Automata , 1951 .

[2] C. Lee Giles,et al. Higher Order Recurrent Networks and Grammatical Inference , 1989, NIPS.

[3] W. Pitts,et al. A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[4] K. P. Unnikrishnan,et al. Nonlinear prediction of speech signals using memory neuron networks , 1991, Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop.

[5] Ah Chung Tsoi,et al. FIR and IIR Synapses, a New Neural Network Architecture for Time Series Modeling , 1991, Neural Computation.