Abstract
Different from gradient neural networks (GNN), a special kind of recurrent neural networks has been proposed recently by Zhang et al for solving online linear matrix equations with time-varying coefficients. Such recurrent neural networks, designed based on a matrix-valued error-function, could achieve global exponential convergence when solving online time-varying problems in comparison with gradient neural networks. This paper investigates the MATLAB simulation of Zhang neural networks (ZNN) for real-time solution of linear time-varying matrix equation AXB − C = 0. Gradient neural networks are simulated and compared as well. Simulation results substantiate the theoretical analysis and efficacy of ZNN on linear time-varying matrix equation solving.
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Chen, K., Yue, S., Zhang, Y. (2008). MATLAB Simulation and Comparison of Zhang Neural Network and Gradient Neural Network for Online Solution of Linear Time-Varying Matrix Equation AXB − C = 0. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_9
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DOI: https://doi.org/10.1007/978-3-540-85984-0_9
Publisher Name: Springer, Berlin, Heidelberg
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