Journal
NEUROCOMPUTING
Volume 177, Issue -, Pages 120-129Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2015.11.014
Keywords
Finite-time stable; Recurrent neural network; Nonlinear optimization problems; Continuous but non-smooth; Hydrothermal scheduling problem
Categories
Funding
- National Science Foundation of China [61374028, 51177088, 61273183]
- Grant National Science Foundation of Hubei Provincial [2013CFA0 50]
- Scientific Innovation Team Project of Hubei Provincial Department of Education [T201504]
Ask authors/readers for more resources
This paper focuses on finite-time recurrent neural networks with continuous but non-smooth activation function solving nonlinearly constrained optimization problems. Firstly, definition of finite-time stability and finite-time convergence criteria are reviewed. Secondly, a finite-time recurrent neural network is proposed to solve the nonlinear optimization problem. It is shown that the proposed recurrent neural network is globally finite-time stable under the condition that the Hessian matrix of the associated Lagrangian function is positive definite. Its output converges to a minimum solution globally and finite time, which means that the actual minimum solution can be derived in finite-time period. In addition, our recurrent neural network is applied to a hydrothermal scheduling problem. Compared with other methods, a lower consumption scheme can be derived in finite-time interval. At last, numerical simulations demonstrate the superiority and effectiveness of our proposed neural networks by solving nonlinear optimization problems with inequality constraints. (C) 2015 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available