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Traditional gradient-based training algorithms have been known to suffer from local minima and have heavy computation load for obtaining the derivative information. The particle swarm optimization (PSO) method was used as a training algorithm of neural networks to improve the convergence rate. However, as the network architecture grows, the size of swarm increases exponentially, which increase the...
Particle swarm optimization (PSO), a new promising evolutionary optimization technique, has a wide range of application in optimization problems including training of artificial neural networks. In this paper, an attempt is made to completely train a RBF neural network architecture including the centers, optimum spreads, and the number of hidden units. The proposed method has been evaluated on some...
Particle swarm optimization and neural networks (PSO- NN) was proposed for twin-spirals scroll compressor (TSSC) performance prediction. The method integrated evolutionary mechanism of PSO and self-learning, nonlinear approach ability of NN. In established NN the input variables were main structure parameters and the output variables were main performance parameters. PSO was used to train NN. The...
Deregulation has created a competitive market among power market participants, and the pricing system plays an important role. Locational marginal pricing (LMP) provides clear market signals that identify the locations where power market participants could make their decisions so as to maximize their profits. In this work, artificial neural networks (ANNs) models are used to predict hourly LMP. ANN...
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