ohua Xia

Letters: Finite time dual neural networks with a tunable activation function for solving quadratic programming problems and its application

In this paper, finite time dual neural networks with a new activation function are presented to solve quadratic programming problems. The activation function has two tunable parameters, which give more flexibility to design the neural networks. By Lyapunov theorem, finite-time stability can be derived for the proposed neural networks, and the actual optimal solutions of the quadratic programming problems can be obtained in finite time interval. Different from the existing recurrent neural networks for solving the quadratic programming problems, the neural networks of this paper have a faster convergent speed, at the same time, they can reduce oscillation when delay appears, and have less sensitivity to additive noise with careful selection of the parameters. Simulations are presented to evaluate the performance of the neural networks with the tunable activation function. In addition, the proposed neural networks are applied to estimate parameters for an energy model of belt conveyors. The effectiveness of our methods are validated by theoretical analysis and numerical simulations.

Distributed optimization of multi-agent systems with delayed sampled-data

Abstract In this paper, we study the distributed optimization problem of multi-agent systems with delayed sampled-data, where the interconnected topology is directed, weighted-balanced and strongly connected, and also local cost functions are strongly convex with globally Lipschitz gradients. Based on synchronous and asynchronous sampled-data, we construct two respective algorithms. Our main results, sufficient conditions for the convergence to an optimal solution, are obtained under assumption that all design parameters are chosen properly. We also present one example to validate our theoretical results.