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In this paper, a robust neural network-based on line learning and artificial immune algorithm is proposed for a boiler combustion optimization system. This method involves a model modification and parameter optimization to the normal use of boiler combustion optimization system neural network. Neural network consists of working sets and standby sets of implicit strata real-time adjusted set number...
With the coal consumption increasing gradually, coal blending is becoming a routine work in power stations. Due to the fluctuation of the coal quality, coal blending is in fact an optimization problem under uncertain conditions, so that it is difficult to solve with the traditional linear programming model. On the other hand, BP neural network, a nonlinear optimization tool, has been successfully...
This paper presents an strategy approach to solve the optimal power flow (OPF) problem for reactive power dispatch which generally requires many power flow calculations. Artificial neural networks are employed to learn in an offline mode and substitute the role of power flow in the OPF which is formulated as a mix integer nonlinear optimization with network loss minimization as the objective. This...
This paper explores the usage of repulsive particle swarm optimization (RPSO) to perform non-linear auto-regressive with exogenous input (NARMAX) system identification of direct current (DC) motor. The NARMAX model was constructed using a recurrent artificial neural network (ANN) model by Rahim and Taib and Yassin et al. The comparison result was made between RPSO method and inertia weight-based PSO...
Support vector machine (SVM) is a new machine learning method based on statistical learning theory, which is a powerful tool for solving the problem with small sample, nonlinear and high dimension. However, the practicability of SVM is affected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social...
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