This paper is concerned with the global asymptotic stability of a class of stochastic bidirectional associative memory neural networks with both multiple discrete and distributed time-varying delays. A new criterion of asymptotic stability is derived in terms of linear matrix inequality, which can be efficiently solved by a standard numerical software. An illustrative numerical example is also given to show the applicability and effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]
In this paper, we first present concepts we developed in order to define an hybrid modeling and simulation methodology dedicated to the study of natural systems. These concepts are based on the object-oriented design, artificial neural networks and the geographic information systems. Then, we outline a general framework combining these three concepts. A first application allowing the forecasting of the hydrologic behavior of watersheds is described and some results are presented and commented. [ABSTRACT FROM AUTHOR]