The Research on the GC Property for RNNs with Limited Matrix 2-Norm

C. Qiao, Rui Zhang, Jing Yao, Xiangliang Kong, Changsheng Zhou
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Abstract

The global convergence (GC) analysis of recurrent neural networks (RNNs) is a first and necessary step for any practical applications of them. In the present paper, when the connecting matrix of the RNNs with projection mapping owning limited norm, the GC property is assured under the critical condition. The results given here not only improve deeply upon the existing relevant critical as well as non-critical dynamics conclusions in literature, but also can be used in the practical application of RNNs directly.
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有限矩阵2-范数rnn的GC性质研究
递归神经网络的全局收敛性分析是递归神经网络实际应用的第一步和必要步骤。当具有投影映射的rnn的连接矩阵具有有限范数时,在临界条件下保证了GC性质。本文的研究结果不仅对文献中已有的临界和非临界动力学的相关结论进行了深入的改进,而且可以直接用于rnn的实际应用。
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