A special kind of recurrent neural networks (RNN) with implicit dynamics has recently been proposed by Zhang et al, which could be generalized to solve online various time-varying problems. In comparison with conventional gradient neural networks (GNN), such RNN (or termed specifically as Zhang neural networks, ZNN) models are elegantly designed by defining matrix-valued indefinite error functions. In this paper, we generalize and investigate the ZNN and GNN models for online solution of time-varying matrix square roots. In addition, software modeling techniques are investigated to model and simulate both neural-network systems. Computer-modeling results verify that superior convergence and efficacy could be achieved by such ZNN models in this time-varying problem solving, as compared to the GNN models. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Zhang, Y., Yang, Y., & Tan, N. (2009). Time-varying matrix square roots solving via zhang neural network and gradient neural network: Modeling, verification and comparison. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5551 LNCS, pp. 11–20). https://doi.org/10.1007/978-3-642-01507-6_2
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