Abstract
Complex-valued matrix inversion problem is investigated by using the gradient-neural-dynamic method. Differing from the traditional processing method (only for real-valued matrix inversion), the proposed method develops a complex gradient neural dynamics for complex-valued matrix inversion in the complex domain. The advantages of the proposed method decrease the complexities in the aspects of computation, analysis, and computer simulations. Theoretical discussions and computer simulations demonstrate the efficacy and superiorness of the proposed method for online the complex-valued matrix inversion in the complex domain, as compared to the traditional processing method.
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Acknowledgment
This work is supported by the Research Foundation of Education Bureau of Hunan Province, China (grant no. 15B192), the Natural Science Foundation of Hunan Province, China (grant no. 2016JJ2101), and the National Natural Science Foundation of China (grant nos. 61503152, 61563017, and 61363073).
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Xiao, L., Liao, B., Zeng, Q., Ding, L., Lu, R. (2017). A Complex Gradient Neural Dynamics for Fast Complex Matrix Inversion. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_61
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DOI: https://doi.org/10.1007/978-3-319-59072-1_61
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