XIAO Lin and LU Rongbo, “A Fully Complex-Valued Gradient Neural Network for Rapidly Computing Complex-Valued Linear Matrix Equations,” Chinese Journal of Electronics, vol. 26, no. 6, pp. 1194-1197, 2017, doi: 10.1049/cje.2017.06.007
Citation: XIAO Lin and LU Rongbo, “A Fully Complex-Valued Gradient Neural Network for Rapidly Computing Complex-Valued Linear Matrix Equations,” Chinese Journal of Electronics, vol. 26, no. 6, pp. 1194-1197, 2017, doi: 10.1049/cje.2017.06.007

A Fully Complex-Valued Gradient Neural Network for Rapidly Computing Complex-Valued Linear Matrix Equations

doi: 10.1049/cje.2017.06.007
Funds:  This work is supported by the National Natural Science Foundation of China (No.61503152, No.61563017, No.61363073), the Natural Science Foundation of Hunan Province, China (No.2016JJ2101, No.2017JJ3258), and the Research Foundation of Education Bureau of Hunan Province, China (No.15B192).
  • Received Date: 2015-09-17
  • Rev Recd Date: 2015-12-18
  • Publish Date: 2017-11-10
  • This paper concerns online solution of complex-valued linear matrix equations in the complex domain. Differing from the real-valued neural network, which is only designed for solving real-valued linear matrix equations in the real domain, a fully complex-valued Gradient neural network (GNN) is developed for computing complex-valued linear matrix equations. The fully complex-valued GNN model has the merit of reducing the unnecessary complexities in theoretical analysis and realtime computation, as compared to the real-valued neural network. Besides, the convergence analysis of the proposed complex-valued GNN model is presented, and simulation experiments are performed to substantiate the effectiveness and superiority of the proposed complex-valued GNN model for online computing the complex-valued linear matrix equations in the complex domain.
  • loading
  • X. Chen and Q. Song, "Global stability of complex-valued neural networks with both leakage time delay and discrete time delay on time scales", Neurocomputing, Vol.121, pp.254-264, 2013.
    K. Subramanian, R. Savitha and S. Suresh, "A complex-valued neuro-fuzzy inference system and its learning mechanism", Neurocomputing, Vol.123, pp.110-120, 2014.
    R. Venkatesh Babu, S. Suresh and R. Savitha, "Human action recognition using a fast learning fully complex-valued classifier", Neurocomputing, Vol.89, pp.202-212, 2012.
    I. Durán-Díaz, S. Cruces, M.A. Sarmiento-Vega, et al., "Cyclic maximization of non-Gaussianity for blind signal extraction of complex-valued sources", Neurocomputing, Vol.74, pp.2867-2873, 2011.
    O. Axelsson and A. Kucherov, "Real valued iterative methods for solving complex symmetric linear systems", Numerical Linear Algebra with Applications, Vol.7, No.4, pp.197-218, 2000.
    F. Ding and T. Chen, "Gradient based iterative algorithms for solving a class of matrix equations", IEEE Transactions on Automatic Control, Vol.50, No.8, pp.1216-1221, 2005.
    B. Zhou, G. Duan and Z. Li, "Gradient based iterative algorithm for solving coupled matrix equations", System Control Letters, Vol.58, No.5, pp.327-333, 2009
    W. Liao, J. Wang and J. Wang, "A recurrent neural network for solving complex-valued quadratic programming problems with equality constraints", Advances in Swarm Intelligence Lecture Notes in Computer Science, Vol.6146, pp.321-326, 2010.
    X. Yang, A. Shen, J. Yang, et al., "Artificial neural network based Trilogic SVM control in current source rectifier", Chinese Journal of Electronics, Vol.23, No.4, pp.723-728, 2014.
    Y. Zhang, Z. Chen and K. Chen, "Convergence properties analysis of gradient neural network for solving online linear equations", Acta Automatica Sinica, Vol.35, No.8, pp.1136-1139, 2009.
    D. Guo, C. Yi and Y. Zhang, "Zhang neural network versus gradient-based neural network for time-varying linear matrix equation solving", Neurocomputing, Vol.74, pp.3708-3712, 2011.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (556) PDF downloads(213) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return