A novel nonzero functional method to extended dissipativity analysis for neural networks with Markovian jumps

A novel nonzero functional method to extended dissipativity analysis for neural networks with Markovian jumps

Blog Article

This paper explored the topic of extended #3.44 DARK COPPER BROWN dissipativity analysis for Markovian jump neural networks (MJNNs) that were influenced by time-varying delays.A distinctive Lyapunov functional, distinguished by a non-zero delay-product types, was presented.This was achieved by combining a Wirtinger-based double integral inequality with a flexible matrix set.This novel methodology addressed the limitations of the slack matrices found in earlier research.As a result, a fresh condition for extended dissipativity in MJNNs was formulated, utilizing an exponential type reciprocally convex inequality in conjunction Washer Hose with the newly introduced nonzero delay-product types.

A numerical example was included to demonstrate the effectiveness of the proposed methodology.