A simple recurrent neural network for solution of linear programming: Application to a Microgrid
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Fecha
2014-12-09
Autores
Loza-López, Martín
Ruiz-Cruz, Riemann
Loukianov, Alexander
Sánchez-Torres, Juan D.
Sánchez-Camperos, Edgar
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Editor
Institute of Electrical and Electronics Engineers
Resumen
Descripción
The aim of this paper is to present a simple new class of recurrent neural networks, which solves 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 finite time stabilizing terms based on the unit control, instead of common used activation functions. Thus, the main feature of the proposed network is the fixed number of parameters despite of the optimization problem dimension, which means, the network can be easily scaled from a small to a higher dimension problem. The applicability of the proposed scheme is tested on real-time optimization of an electrical Microgrid prototype.
Palabras clave
Microgrids, Recurrent Neural Networks
Citación
Sánchez-Torres, J.D.; Loza-Lopez, M.J.; Ruiz-Cruz, R.; Sanchez, E.N.; Loukianov, A.G., "A simple recurrent neural network for solution of linear programming: Application to a Microgrid,"Computational Intelligence Applications in Smart Grid (CIASG), 2014 IEEE Symposium on, Orlando, USA, Dec. 2014, pp.1,7, 9-12.