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A new neural paradigm called diagonal recurrent neural network (DRNN) is presented. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer, and the hidden layer comprises self-recurrent neurons. Two DRNN's are utilized in a control system, one as an identifier called diagonal recurrent(More)
—This paper presents a new approach to economic dispatch (ED) problems with nonsmooth cost functions using a particle swarm optimization (PSO) technique. The practical ED problems have nonsmooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. In this(More)
—This paper presents an efficient approach for solving economic dispatch (ED) problems with nonconvex cost functions using an improved particle swarm optimization (IPSO). Although the particle swarm optimization (PSO) approaches have several advantages suitable to heavily constrained nonconvex optimization problems, they still can have the drawbacks such as(More)
The authors are very grateful to the discussers for their great interest in our paper and discussion on the paper. In our paper [1], we used data from Coelho and Mariani [2] considering as corrected ones. Even when we use the data of Table I in the discussion, we obtain the results of Table III below. In this case, our results for total power 1800 MW and(More)
—Multiobjective optimal power plant operation requires an optimal mapping between unit load demand and pressure set point in a fossil fuel power unit (FFPU). In general, the optimization problem with varying unit load demand cannot be solved using a fixed nonlinear mapping. This paper presents a modern heuristic method, particle swarm optimization (PSO), to(More)
paper proposes a new binary particle swarm optimization (BPSO) approach inspired from quantum computing, so-called quantum-inspired BPSO (QBPSO), for solving the unit commitment (UC) problems. Although BPSO-based approaches have been successfully applied to the combinatorial optimization problems of power systems, the BPSO algorithm has some drawbacks such(More)
Keywords: Boiler–turbine system Model predictive control TS fuzzy model Genetic algorithm (GA) Terminal cost a b s t r a c t This paper presents a model predictive control (MPC) strategy based on genetic algorithm to solve the boiler–turbine control problem. First, a Takagi–Sugeno (TS) fuzzy model based on gap values is established to approximate the(More)
Keywords: Multi-objective optimization Parallel VEPSO Reactive power control a b s t r a c t In this paper the state-of-the-art extended particle swarm optimization (PSO) methods for solving multi-objective optimization problems are represented. We emphasize in those, the co-evolution technique of the parallel vector evaluated PSO (VEPSO), analysed and(More)
—This paper presents the ant colony system (ACS) method for network-constrained optimization problems. The developed ACS algorithm formulates the constrained load flow (CLF) problem as a combinatorial optimization problem. It is a distributed algorithm composed of a set of cooperating artificial agents, called ants, that cooperate among them to find an(More)