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Although particle swam optimization (PSO) algorithm is a good optimization tool for feedforward neural network s(FNN), it is easy to lose the diversity of the swarm and suffer from premature convergence. An improved PSO algorithm based on the attractive and repulsive PSO (ARPSO) is proposed to train FNN in this paper. In addition to the phases of repulsion and attraction, the third phase named as...
In this paper, an improved particle swarm optimization (PSO) with the improved diversity is proposed to train feedforward neural networks (FNN). In this algorithm, first, the PSO algorithm is used to train the FNN. Second, when the particle swarm is trapped into local minima or loses its diversity, each particle in the swarm and its best position (Pb) are interrupted by a random function in order...
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