Convergence of an Online Split-Complex Gradient Algorithm for Complex-Valued Neural Networks | Zendy

Huisheng Zhang | Zendy; Dongpo Xu | Zendy; Zhiping Wang | Zendy

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Convergence of an Online Split-Complex Gradient Algorithm for Complex-Valued Neural Networks

Author(s) -

Huisheng Zhang,

Dongpo Xu,

Zhiping Wang

Publication year - 2010

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2010/829692

Subject(s) - monotonic function , artificial neural network , convergence (economics) , computer science , sequence (biology) , algorithm , function (biology) , error function , gradient method , zero (linguistics) , gradient descent , mathematics , artificial intelligence , mathematical analysis , linguistics , philosophy , evolutionary biology , biology , economics , genetics , economic growth

The online gradient method has been widely used in training neural networks. We consider in this paper an online split-complex gradient algorithm for complex-valued neural networks. We choose an adaptive learning rate during the training procedure. Under certain conditions, by firstly showing the monotonicity of the error function, it is proved that the gradient of the error function tends to zero and the weight sequence tends to a fixed point. A numerical example is given to support the theoretical findings

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