Finite-Time Estimation for Markovian BAM Neural Networks With Asymmetrical Mode-Dependent Delays and Inconstant Measurements. Academic Article

abstract

• The issue of finite-time state estimation is studied for discrete-time Markovian bidirectional associative memory neural networks. The asymmetrical system mode-dependent (SMD) time-varying delays (TVDs) are considered, which means that the interval of TVDs is SMD. Because the sensors are inevitably influenced by the measurement environments and indirectly influenced by the system mode, a Markov chain, whose transition probability matrix is SMD, is used to describe the inconstant measurement. A nonfragile estimator is designed to improve the robustness of the estimator. The stochastically finite-time bounded stability is guaranteed under certain conditions. Finally, an example is used to clarify the effectiveness of the state estimation.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Neural Netw Learn Syst

author list (cited authors)

• Liu, C., Wang, Z., Lu, R., Huang, T., & Xu, Y.

citation count

• 4

complete list of authors

• Liu, Chang||Wang, Zhuo||Lu, Renquan||Huang, Tingwen||Xu, Yong

publication date

• January 2023

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• n2162-237XISSN  Journal

keywords

• Artificial Neural Networks
• Delay Effects
• Delays
• Finite-time Bounded
• Markov Processes
• Markovian Bidirectional Associative Memory Neural Networks (bam Nns)
• Sensors
• State Estimation
• Temperature Measurement
• Time-varying Delays (tvds)

PubMed Central ID

• 34270434

Digital Object Identifier (DOI)

• 10.1109/TNNLS.2021.3094551

start page

• 344

end page

• 354

volume

• 34

issue

• 1

URL

• http://dx.doi.org/10.1109/tnnls.2021.3094551