Abstract
An improved complex varying-parameter Zhang neural network (CVPZNN) for computing outer inverses is established in this paper. As a consequence, three types of complex Zhang functions (ZFs) which are used for computing the time-varying core-EP inverse and core inverse are given. The convergence rate of the proposed complex varying-parameter Zhang neural networks (CVPZNNs) is accelerated. The super-exponential performance of the proposed CVPZNNs with linear activation is proved. Also, the upper bounds of a finite time convergence which correspond to the proposed CVPZNN with underlying Li and tunable activation functions are estimated. The simulation results, which relate the CVPZNNs with different activation functions, are presented.
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Acknowledgements
The authors thank the editor and reviewers, sincerely, for their constructive comments and suggestions that have improved the quality of the paper. This research is supported by the National Natural Science Foundation of China (No. 11771076); the Postgraduate Research and Practice Innovation Program of Jiangsu Province (No. KYCX18\(_{-}\)0053). Also, the research is supported by the bilateral project “The theory of tensors, operator matrices and applications (No. 4–5)” between China and Serbia. Predrag S. Stanimirović gratefully acknowledge support from the Ministry of Education and Science, Republic of Serbia, Grant No. 174013. Haifeng Ma is supported by the bilateral project between China and Poland (No. 37-18).
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Zhou, M., Chen, J., Stanimirović, P.S. et al. Complex Varying-Parameter Zhang Neural Networks for Computing Core and Core-EP Inverse. Neural Process Lett 51, 1299–1329 (2020). https://doi.org/10.1007/s11063-019-10141-6
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DOI: https://doi.org/10.1007/s11063-019-10141-6
Keywords
- Outer inverse
- Core-EP inverse
- Core inverse
- Complex varying-parameter Zhang neural network
- Super-exponential convergence