Photo by jontyson from unsplash
In this paper, the dissipativity problem of discrete-time memristive neural networks (DMNNs) with time-varying delays and stochastic perturbation is investigated. A class of logical switched functions are put forward to… Click to show full abstract
In this paper, the dissipativity problem of discrete-time memristive neural networks (DMNNs) with time-varying delays and stochastic perturbation is investigated. A class of logical switched functions are put forward to reflect the memristor-based switched property of connection weights, and the DMNNs are then recast into a tractable model. Based on the tractable model, the robust analysis method and Refined Jensen-based inequalities are applied to establish some sufficient conditions that ensure the $(\mathcal {Q},\mathcal {S},\mathcal {R})-\gamma -\text {disspativity}$ of DMNNs. Two numerical examples are presented to illustrate the effectiveness of the obtained results.
Journal Title: IEEE Transactions on Neural Networks and Learning Systems
Year Published: 2018
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!