A recurrent neural network for real time electrical microgrid prototype optimization
Cargando...
Archivos
Fecha
2014-07-06
Autores
Loza-López, Martín
Ruiz-Cruz, Riemann
Loukianov, Alexander
Sánchez-Torres, Juan D.
Sánchez-Camperos, Edgar
Título de la revista
ISSN de la revista
Título del volumen
Editor
Institute of Electrical and Electronics Engineers
Resumen
Descripción
The aim of this paper is to present a new class of recurrent neural networks, which solve linear programming. It is considered as a sliding mode control problem, where the network structure is based on the Karush-Kuhn-Tucker (KKT) optimality conditions, and the KKT multipliers are the control inputs to be implemented with fixed time stabilizing terms, instead of common used activation functions. Thus, the main feature of the proposed network is its fixed convergence time to the solution, which means, there it is a time independent to the initial conditions in which the network converges to the optimization solution. The applicability of the proposed scheme is tested on real-time optimization of an electrical microgrid prototype.
Palabras clave
Microgrids, Fixed Time Stability, Recurrent Neural Networks, Linear Programming
Citación
Sánchez-Torres, J.D.; Loza-López, M.J.; Ruiz-Cruz, R.; Sánchez, E.N.; Loukianov, A.G., "A recurrent neural network for real time electrical microgrid prototype optimization,"Neural Networks (IJCNN), 2014 International Joint Conference on, Beijing, China, 6-11 July 2014, pp.2794,2799.