LMI approach for global robust stability of Cohen–Grossberg neural networks with multiple delays

Ji, Ce; Zhang, H. G.; Wei, Yuan

doi:10.1016/j.neucom.2007.07.014

Cited by 22 publications

(26 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is expected that a larger value of CINE means an increased barrier to convection. If CINE is large, deep clouds will not develop even if CAPE is positive, while low values of CINE imply a favorable condition for convection [12].…”

Section: Methodsmentioning

confidence: 99%

New Cloud Detection Index (CDI) for Forecasting the Extreme Rain Events

Shastri¹,

Pathak²

2019

ARS

View full text Add to dashboard Cite

The water vapor content and clouds play a very significant role in atmospheric conditions. This paper is derived from the new cloud detection index (CDI) which is useful to forecast extreme weather events like heavy or extreme rain. The CDI is retrieved using two crucial elements of cloud formation, the critical humidity and critical water vapor. The cloud thickness is determined by using CDI for a radiosonde site (VABB) located in Mumbai, the western part of India. The obtained results are compared with the cloud thickness required for extreme rain. The outcome of the comparison is discussed in this paper. The cloud detection index is also useful in establishing the atmospheric stability along with another four atmospheric stability indices.

show abstract

Section: Methodsmentioning

confidence: 99%

New Cloud Detection Index (CDI) for Forecasting the Extreme Rain Events

Shastri¹,

Pathak²

2019

ARS

View full text Add to dashboard Cite

show abstract

“…Note that Assumptions 2.1-2.3 are standard as those, for example, in [6]- [8], [10], [12]- [15], [19]- [21], [29]- [31], and [33]- [35]. Assumption 2.1 is used to keep the solution of Cohen-Grossberg neural network model well behaved.…”

Section: Assumption 22mentioning

confidence: 99%

“…In general, there are mainly four kinds of forms of stability conditions for delayed neural networks, for example, linear matrix inequality (LMI) form [2], [3], [13], [15], [24], [28], [37], [40], M -matrix form [6], [14], [29], matrix norm form [12], and algebraic inequality form [7], [10], [19]. In this brief, different from the methods used above, a new sufficient condition is derived to ensure the global robust stability of system (3) via the concept of state transmission matrix.…”

Section: Introductionmentioning

confidence: 99%

Robust Stability of Cohen–Grossberg Neural Networks via State Transmission Matrix

Wang

Zhang

2009

IEEE Trans. Neural Netw.

View full text Add to dashboard Cite

This brief is concerned with the global robust exponential stability of a class of interval Cohen-Grossberg neural networks with both multiple time-varying delays and continuously distributed delays. Some new sufficient robust stability conditions are established in the form of state transmission matrix, which are different from the existing ones. Furthermore, a sufficient condition is also established to guarantee the global stability for this class of Cohen-Grossberg neural networks without uncertainties. Three examples are used to show the effectiveness of the obtained results.

show abstract

“…During the implementation of artificial neural networks on verylarge-scale integrated chips, transmitting time delays are unavoidable and may cause undesirable dynamic network behaviors, such as oscillation and instability. Hence, it is important to consider the stability of CGNNs with delays [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In real nerve systems, a neural network could be stabilized or destabilized by certain stochastic inputs [18].…”

Section: Introductionmentioning

confidence: 99%