期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 29, 期 10, 页码 5149-5158出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2018.2793185
关键词
Dissipative estimator; distributed delays; Markov chain; neural networks; packet dropouts
类别
资金
- National Natural Science Foundation of China [61503106, U1611262]
- China National Funds for Distinguished Young Scientists [61425009]
- Fundamental Research Funds for the Central Universities [2017FZA5010]
- Guangdong Province Higher Vocational Colleges and Schools Pearl River Scholar
- Guangdong Province Higher Vocational Colleges and Schools Pearl River Young Scholar
This paper investigates the estimator design for the neural networks, where distributed delays and imperfect measurements are included. A randomly occurred neuron-dependent nonlinearity is used to describe the uncertain measurements disturbed by neurons. The measurements are transmitted over multiple transmission channels, and Markov chains are introduced to model packet dropouts of these channels. A one-toone map is constructed to transform m independent Markov chains to an augmented one to facilitate system analysis. A new variable called channel state is defined based on the cases of packet dropouts, and the channel-state-dependent estimator is designed to trade off between the number and the performance of the estimator. Sufficient conditions are established to guarantee that the augmented system is stochastically stable and satisfies the strict (Q, S, R)-gamma-dissipativity. The estimator gains are derived using linear matrix methods. Finally, an example is applied to illustrate the effectiveness of the developed methods.
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