An improved particle swarm optimization algorithm used for BP neural network and multimedia course-ware evaluation

Tianhua Liu, Shoulin Yin
<span title="2016-07-29">2016</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7inqmh346zfjjizjyieh7ijtma" style="color: black;">Multimedia tools and applications</a> </i> &nbsp;
The original BP neural network has some disadvantages, such as slow convergence speed, low precision, which is easy to fall into local minimum value. So this paper proposes an improved particle swarm optimization (PSO) algorithm to optimize BP neural network. In this new algorithm, PSO uses improved adaptive acceleration factor and improved adaptive inertia weight to improve the initial weight value and threshold value of BP neural network. And we give the detailed improved process. At the end,
more &raquo; ... simulation results show that the new algorithm can improve convergence rate and precision of prediction of BP neural network, which reduces the error of prediction. At the end, we use multimedia evaluation model to verify the new method's performance.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11042-016-3776-5">doi:10.1007/s11042-016-3776-5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tdwemmgclfcwbhr5ad4iwyjc4q">fatcat:tdwemmgclfcwbhr5ad4iwyjc4q</a> </span>
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