Biomedical subjects

James Lam

Publications and source records attributed to James Lam.

6 recordsLinked to original sources

PubMed2006

Global exponential stability of impulsive high-order BAM neural networks with time-varying delays.

In this paper, global exponential stability and exponential convergence are studied for a class of impulsive high-order bidirectional associative memory (BAM) neural networks with time-varying delays. By employing linear matrix inequalities (LMIs) and differential inequalities with delays and impulses, several sufficient conditions are obtained for ensuring the system to be globally exponentially stable. Three illustrative examples are also given at the end of this paper to show the effectiveness of our results.

Algorithms↗

PubMed2006

Global point dissipativity of neural networks with mixed time-varying delays.

By employing the Lyapunov method and some inequality techniques, the global point dissipativity is studied for neural networks with both discrete time-varying delays and distributed time-varying delays. Simple sufficient conditions are given for checking the global point dissipativity of neural networks with mixed time-varying delays. The proposed linear matrix inequality approach is computationally efficient as it can be solved numerically using standard commercial software. Illustrated examples are given to show the usefulness of the results in comparison with some existing results.

Action Potentials↗

PubMed2005

A new approach to exponential stability analysis of neural networks with time-varying delays.

This paper considers the problem of exponential stability analysis of neural networks with time-varying delays. The activation functions are assumed to be globally Lipschitz continuous. A linear matrix inequality (LMI) approach is developed to derive sufficient conditions ensuring the delayed neural network to have a unique equilibrium point, which is globally exponentially stable. The proposed LMI conditions can be checked easily by recently developed algorithms solving LMIs. Examples are provided to demonstrate the reduced conservativeness of the proposed results.

Algorithms↗

PubMed2005

Observer design and stabilization for linear neutral delay systems.

This paper focuses on the state observer design problem as well as the observer-based stabilization problem for linear neutral delay systems. The purpose of the former problem is to design an observer that guarantees the asymptotic stability of the estimation error dynamics. The existence condition for such an observer is established. The latter problem, which is the main problem studied in this paper, aims at designing an observer-based feedback controller, such that the closed-loop system is asymptotically stabilized. It is shown that the desired controller can be easily designed if there are solutions to several linear matrix inequalities. Finally, two simulation examples are given to demonstrate the validity and effectiveness of the proposed approach.

Journal Article↗

PubMed2005

Exponential epsilon-regulation for multi-input nonlinear systems using neural networks.

This paper considers the problem of robust exponential epsilon-regulation for a class of multi-input nonlinear systems with uncertainties. The uncertainties appear not only in the feedback channel but also in the control channel. Under some mild assumptions, an adaptive neural network control scheme is developed such that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded and, under the control scheme with initial data starting in some compact set, the states of the closed-loop system is guaranteed to exponentially converge to an arbitrarily specified epsilon-neighborhood about the origin. The important contributions of the present work are that a new exponential uniformly ultimately bounded performance is proposed and that the design parameters and initial condition set can be determined easily. The development generalizes and improves earlier results for the single-input case.

Algorithms↗

PubMed2005

Improved global robust asymptotic stability criteria for delayed cellular neural networks.

This paper considers the problem of global robust stability analysis of delayed cellular neural networks (DCNNs) with norm-bounded parameter uncertainties. In terms of a linear matrix inequality, a new sufficient condition ensuring a nominal DCNN to have a unique equilibrium point which is globally asymptotically stable is proposed. This condition is shown to be a generalization and improvement over some previous criteria. Based on the stability result, a robust stability condition is developed, which contains an existing robust stability result as a special case. An example is provided to demonstrate the reduced conservativeness of the proposed results.

Algorithms↗