Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 15, Issue 1, Pages 236-246Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2018.2798642
Keywords
Control techniques; neural algorithm; noise reduction; residual error; system of linear equations
Categories
Funding
- National Natural Science Foundation of China [61703189, 61632014, 61210010]
- National Basic Research Program of China (973 Program) [2014CB744600]
- Program of Beijing Municipal Science and Technology Commission [Z171100000117005]
- Program of International SAMP
- T Cooperation of MOST [2013DFA11140]
- Hong Kong Research Grants Council Early Career Scheme [25214015]
- Hong Kong Polytechnic University [G-YBMU, G-UA7L, 4-ZZHD, F-PP2C, 4-BCCS]
- Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province
- Fundamental Research Funds for the Central Universities [lzujbky-2017-37]
- Hunan Natural Science Foundation of China [2017JJ3257, 2017JJ3258]
- Research Foundation of Education Bureau of Hunan Province, China [17B215, 17C1299]
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It has been found that there exists an essential similarity between solving equations and controlling dynamic systems: Both errors are expected to decrease to zero (or an acceptably tiny value) as soon as possible. By exploiting such a similarity, researchers have presented and investigated continuous-time recurrent neural network models for solving time-varying problems. To be compatible with digital computers, it is desirable to develop discrete-time neural algorithms from the control perspective for performance improvement. In this paper, a discrete-time zeroing neural algorithm is proposed for the solving system of linear equations with the aid of control techniques. To lay a basis for theoretical analyses, the proposed zeroing neural algorithm with nonlinearity is converted into a second-order linear system plus a residual term, and then, analyzed using the control theory. Theoretical results and numerical experiments are provided, which illustrate that the proposed neural algorithm possesses an improved performance compared to the existing solutions.
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