Abstract
Differing from gradient-based neural networks (GNN), In this paper, we present a special kind of recurrent neural networks using a new design method to solve online the time-varying Stein matrix equation A(t)X(t)B(t) + X(t) = C(t). This paper investigates simulation and verification of the resultant Zhang neural networks (ZNN) for the nonstationary Stein equation by using MATLAB simulation techniques. Theoretical analysis and simulation results substantiate the superior performance of the ZNN models for the solution of time-varying Stein equation in real-time, in compared with the GNN models.
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© 2011 Springer-Verlag Berlin Heidelberg
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Yi, C., Chen, Y., Wang, H. (2011). Simulation and Verification of Zhang Neural Networks and Gradient Neural Networks for Time-Varying Stein Equation Solving. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21105-8_45
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DOI: https://doi.org/10.1007/978-3-642-21105-8_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21104-1
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