Stability Analysis and State Estimation of Memristive Neural Networks

By: Hongjian Liu, Zidong Wang, Lifeng Ma

Write A Review

eText | 16 August 2021 | Edition Number 1

At a Glance

Format
ePUB

eText

$96.79

or 4 interest-free payments of $24.20 with

Instant online reading in your Booktopia eTextbook Library *

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

In this book, the stability analysis and estimator design problems are discussed for delayed discrete-time memristive neural networks. In each chapter, the analysis problems are firstly considered, where the stability, synchronization and other performances (e.g., robustness, disturbances attenuation level) are investigated within a unified theoretical framework. In this stage, some novel notions are put forward to reflect the engineering practice. Then, the estimator design issues are discussed where sufficient conditions are derived to ensure the existence of the desired estimators with guaranteed performances. Finally, the theories and techniques developed in previous parts are applied to deal with some issues in several emerging research areas.

The book

Unifies existing and emerging concepts concerning delayed discrete memristive neural networks with an emphasis on a variety of network-induced phenomena
Captures recent advances of theories, techniques, and applications of delayed discrete memristive neural networks from a network-oriented perspective
Provides a series of latest results in two popular yet interrelated areas, stability analysis and state estimation of neural networks
Exploits a unified framework for analysis and synthesis by designing new tools and techniques in combination with conventional theories of systems science, control engineering and signal processing
Gives simulation examples in each chapter to reflect the engineering practice