A Discrete-Time Recurrent Neural Network for Solving Rank-Deficient Matrix Equations With an Application to Output Regulation of Linear Systems
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AbstractThis paper presents a discrete-time recurrent neural network approach to solving systems of linear equations with two features. First, the system of linear equations may not have a unique solution. Second, the system matrix is not known precisely, but a sequence of matrices that converges to the unknown system matrix exponentially is known. The problem is motivated from solving the output regulation problem for linear systems. Thus, an application of our main result leads to an online solution to the output regulation problem for linear systems.
All Author(s) ListTao Liu, Jie Huang
Journal nameIEEE Transactions on Neural Networks and Learning Systems
Year2018
Month6
Volume Number29
Issue Number6
PublisherIEEE
Pages2271 - 2277
ISSN2162-237X
eISSN2162-2388
LanguagesEnglish-United Kingdom
KeywordsDiscrete-time systems, linear output regulation, recurrent neural networks
Web of Science Subject CategoriesComputer Science, Artificial Intelligence;Computer Science, Hardware & Architecture;Computer Science, Theory & Methods;Engineering, Electrical & Electronic;Computer Science;Engineering

Last updated on 2020-04-04 at 11:39