Synchronization for Quantized Semi-Markov Switching Neural Networks in a Finite Time

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 3; pp. 1264 - 1275
• Main Authors: Qi, Wenhai, Park, Ju H., Zong, Guangdeng, Cao, Jinde, Cheng, Jun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Control systems design; Feedback control; Finite-time controller; Linear matrix inequalities; logarithmic quantizer; Markov processes; Mathematical analysis; Neural networks; Stability analysis; Stochasticity; Switched mode power supplies; Switches; Switching; Synchronization; Time synchronization; weak infinitesimal operator
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.2984040

Finite-time synchronization (FTS) is discussed for delayed semi-Markov switching neural networks (S-MSNNs) with quantized measurement, in which a logarithmic quantizer is employed. The stochastic phenomena of structural and parametrical changes are modeled by a semi-Markov process whose transition rates are time-varying to depend on the sojourn time. Practical systems subject to unpredictable structural changes, such as quadruple-tank process systems, are described by delayed S-MSNNs. A key issue under the consideration is how to design a feedback controller to guarantee the FTS between the master system and the slave system. For this purpose, by using the weak infinitesimal operator, sufficient conditions are constructed to realize FTS of the resulting error system over a finite-time interval. Then, the solvability conditions for the desired finite-time controller can be determined under a linear matrix inequality framework. Finally, the theoretical findings are illustrated by the quadruple-tank process model.