Stability Of Bidirectional Associative Memory Research Articles

Global exponential p-norm stability of BAM neural networks with unbounded time-varying delays: A method based on the representation of solutions

This paper studies the global exponential [Formula: see text]-norm stability of bidirectional associative memory (BAM) neural networks with unbounded time-varying delays. A novel method based on the representation of solutions is put forward to deduce a global exponential [Formula: see text]-norm stability criterion. This method does not need to set up any Lyapunov–Krasovskii functionals (LKF), which can greatly reduce a large amount of computations and is simpler than the existing methods. In the end, representative numerical examples are given to illustrate the availability of the method.

The Sampled-data Exponential Stability of BAM with Distributed Leakage Delays

This paper investigates the exponential stability of bidirectional associative memory (BAM) neural networks with distributed leakage delays and sampled-data state feedback input. Based on the input delay approach, the considered BAM neural networks is transformed into a system with mixed distributed leakage delays and time-varying discrete delays. Some sufficient conditions are given to ensure that the system is exponentially stable. Finally, a numerical example is provided to demonstrate the effectiveness of the theoretical analysis.

Further results on robust stability of bidirectional associative memory neural networks with norm-bounded uncertainties

Abstract In this paper, we have a further study about global robust stability of dynamical bidirectional associative memory (BAM) neural networks with norm-bounded uncertainties. By introducing four new upper bound norms for the interconnection matrices of the neural networks and constructing a suitable Lyapunov functional, several new criteria on global robust stability are established. The obtained results can be easily verified as they can be expressed in terms of the network parameters only. In comparison with the results reported in the literature, the proposed approach leads to results with less restrictive conditions. Two numerical examples are also worked through to illustrate our results.

Global robust asymptotic stability analysis of BAM neural networks with time delay and impulse: An LMI approach

The global robust asymptotic stability of bi-directional associative memory (BAM) neural networks with constant or time-varying delays and impulse is studied. An approach combining the Lyapunov–Krasovskii functional with the linear matrix inequality (LMI) is taken to study the problem. Some a criteria for the global robust asymptotic stability, which gives information on the delay-dependent property, are derived. Some illustrative examples are given to demonstrate the effectiveness of the obtained results.

DELAY-DEPENDENT STABILITY CRITERION FOR BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH INTERVAL TIME-VARYING DELAYS

In the letter, the global asymptotic stability of bidirectional associative memory (BAM) neural networks with delays is investigated. The delay is assumed to be time-varying and belongs to a given interval. A novel stability criterion for the stability is presented based on the Lyapunov method. The criterion is represented in terms of linear matrix inequality (LMI), which can be solved easily by various optimization algorithms. Two numerical examples are illustrated to show the effectiveness of our new result.

On improved delay-dependent criterion for global stability of bidirectional associative memory neural networks with time-varying delays

In the paper, the global robust asymptotic stability of bidirectional associative memory (BAM) neural networks with time-varying delays and uncertainties is investigated. A novel stability criterion is presented based on the Lyapunov method. The criterion is represented in terms of linear matrix inequality (LMI), which can be solved easily by various optimization algorithms. A numerical example is illustrated to show the effectiveness of our new result.

DELAY-DEPENDENT GLOBAL ROBUST ASYMPTOTIC STABILITY ANALYSIS OF BAM NEURAL NETWORKS WITH TIME DELAY: AN LMI APPROACH

The global robust asymptotic stability of bi-directional associative memory (BAM) neural networks with constant or time-varying delays is studied. An approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI) is taken to study the problem. Some a criteria for the global robust asymptotic stability, which gives information on the delay-dependent property, are derived. Some illustrative examples are given to demonstrate the effectiveness of the obtained results.

Global exponential stability of BAM neural networks with distributed delays and reaction–diffusion terms

Two sufficient conditions, which ensure the existence and uniqueness of the equilibrium point and global exponential stability of bi-directional associative memory (BAM) neural networks with distributed delays and reaction–diffusion terms, are obtained by using the theory of topological degree, properties of M-matrix, Lyapunov functional, and analysis technique. The two sufficient conditions are independent of each other. The results remove the usual assumption that the activation functions are of bounded character. Exponential converging velocity index is estimated, which depends on the delay kernel functions and system parameters. Two numerical examples are given to show the correctness of our analysis. These results can be applied to design globally exponentially stable networks and thus have important significance in both theory and applications.