• Comparing with the other two algorithms, the QPSO can converge rapidly and search out the global optima most frequently.

    跟其它两种算法相比,QP SO收敛的速度快并且最容易找到全局最优解。

    youdao

  • Not only QPSO has less parameters to control, but also does its sampling space at each iteration covers the whole solution space.

    QP SO不仅参数个数少,其每一个迭代步的取样空间能覆盖整个解空间,因此能保证算法的全局收敛。

    youdao

  • Three images results of segmentation are presented and demonstrate the efficiency of QPSO algorithms to automatic and unsupervised texture segmentation.

    给出了三幅图像的分割效果,证明了QPSO算法在自动的和无监督的纹理分割上具有很好的效果。

    youdao

  • To improving the diagnosed speed and accuracy of the steam turbine diagnose system, this paper proposes a method that combines QPSO with BP neural networks.

    为了提高汽轮机诊断系统的诊断速度与精度,提出了将量子粒子群算法和BP神经网络相结合的故障诊断方法。

    youdao

  • In the web anomaly intrusion detection, the application of the WLS-SVM based on QPSO algorithm, the results is significantly, especially to small sample size of data has good predictive ability.

    在网络异常入侵检测中,应用基于QPSO的WLS - SVM算法效果显著,特别是对于小样本数据量具有良好的预测能力。

    youdao

  • According to still existing problem of Quantum-behaved Particle Swarm Optimization (QPSO), a new QPSO with two Particle Swarms based on public history researching side-by-side (TPHQPSO) is presented.

    针对量子粒子群算法存在的问题,设计基于公共历史的两种群并行搜索的量子粒子群算法。

    youdao

  • According to still existing problem of Quantum-behaved Particle Swarm Optimization (QPSO), a new QPSO with two Particle Swarms based on public history researching side-by-side (TPHQPSO) is presented.

    针对量子粒子群算法存在的问题,设计基于公共历史的两种群并行搜索的量子粒子群算法。

    youdao

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