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

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This paper explored the topic of extended dissipativity analysis for Markovian jump neural click here 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 jilungin dreaming tea 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 with the newly introduced nonzero delay-product types.A numerical example was included to demonstrate the effectiveness of the proposed methodology.