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This paper presents the power supply on-line optimization in a Microgrid prototype. The power supply is formulated as a linear programming problem. With this formulation, a new recurrent neural network which solves linear programming is proposed and applied to the supply problem. The main features of the proposed system are the fixed convergence time to the programming solution and the fixed parameters...
The aim of this paper is to introduce a new recurrent neural network to solve linear programming. The main characteristic of the proposed scheme is its design based on the predefined-time stability. The predefined-time stability is a stronger form of finite-time stability which allows the a priori definition of a convergence time that does not depend on the network initial state. The network structure...
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...
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