期刊
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
卷 12, 期 3, 页码 575-587出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCDS.2019.2948066
关键词
Mathematical model; Time-varying systems; Biological neural networks; Circadian rhythm; Noise reduction; Circadian rhythms; convergence; large errors; neural network; online equation solving; time-varying problem
资金
- National Natural Science Foundation [61976096, 61603142, 61633010]
- Guangdong Foundation for Distinguished Young Scholars [2017A030306009]
- Guangdong Special Support Program [2017TQ04X475]
- Science and Technology Program of Guangzhou [201707010225]
- Fundamental Research Funds for Central Universities [x2zdD2182410]
- Scientific Research Starting Foundation of South China University of Technology
- National Key Research and Development Program of China [2017YFB1002505]
- National Key Basic Research Program of China (973 Program) [2015CB351703]
- Guangdong Natural Science Foundation [2014A030312005]
Time-varying dynamic system contaminated by cognitive noises is universal in the fields of engineering and science. In this article, a circadian rhythms learning network (CRLN) is proposed and investigated for disposing the noise disturbed time-varying dynamic system. To do so, a vector-error function is first defined. Second, a neural dynamic model is formulated. Third, a co-state matrix is integrated into the model, of which the states are the linear combination of the previous periodic states and errors, which can effectively suppress periodic noises. Theoretical analysis and mathematical derivation prove the global exponential convergence performance of the proposed CRLN model. Finally, a practical noise disturbed time-varying dynamic system example with four different noises illustrates the accuracy and efficacy of the proposed CRLN model. Comparisons with traditional zeroing neural network further verify the advantages of the proposed CRLN model.
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