Reduced-Order Filtering of Delayed Static Neural Networks With Markovian Jumping Parameters. Academic Article

abstract

• The reduced-order filtering problems are investigated in this paper for static neural networks with Markovian jumping parameters and mode-dependent time-varying delays. By fully making use of integral inequalities, the designs of reduced-order and filters are discussed. The proper gain matrices of filters and the optimal performance indices are efficiently obtained by resolving corresponding convex optimization problems with the constraints of linear matrix inequalities. It is verified that the computational complexity for the reduced-order filter design is significantly reduced when compared with the full-order one. Furthermore, the nonfragile reduced-order filtering problems are also resolved in this paper. Two examples with simulation results are presented to demonstrate the feasibility and application of the established results.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Neural Netw Learn Syst

author list (cited authors)

• Huang, H. e., Huang, T., & Cao, Y.

citation count

• 19

complete list of authors

• Huang, He||Huang, Tingwen||Cao, Yang

publication date

• November 2018

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• n2162-237XISSN  Journal

keywords

• Markovian Jumping Parameters
• Mode-dependent Time Delays
• Performance Analysis
• Reduced-order Filters
• Static Neural Networks (snns)

PubMed Central ID

• 29994081

Digital Object Identifier (DOI)

• 10.1109/TNNLS.2018.2806356

start page

• 5606

end page

• 5618

volume

• 29

issue

• 11