QPSO算法的改进及其在DBN参数优化中应用

为了提升标准量子粒子群算法（Quantum Particle Swarm Optimization，QPSO）的收敛精度，提出了基于粒子势阱长度变化率的粒子平均最优位置权重计算方法，通过平均最优位置的调节，来提升粒子的寻优能力，并将改进后的QPSO算法应用于深度置信网络（Depth Belief Network，DBN）模型的学习率参数寻优中，以便找到最优的DBN模型参数，来提升DBN模型的识别准确率。将通过改进后QPSO算法进行参数寻优的DBN网络（LQ_DBN）应用于蛋黄形状检测中，与现有典型的DBN网络模型对比表明，LQ_DBN模型在蛋黄形状检测实验中的识别准确率比CC-PSO-DBN、PSO_MDBN和标准DBN模型都要高，且检测识别准确率的稳定性也是四种对比模型中最高的，表明基于改进的QPSO算法的DBN网络模型取得了较好的优化效果。

Improvement of QPSO Algorithm and Its Application in DBN Parameter Optimization

In order to improve the convergence accuracy of the standard Quantum Particle Swarm Optimization（QPSO） algorithm, a calculation method of the average optimal position weight of particles based on the change rate of the length of the particle potential well is proposed. By adjusting the average optimal position, the optimization ability of particles is improved. The improved QPSO algorithm is applied to the optimization of the learning rate parameters of the Depth Belief Network（DBN） model, so as to find the optimal DBN model parameters to improve the recognition accuracy of the DBN model. Finally, the DBN network （LQ_DBN）, which is optimized by the improved QPSO algorithm, is applied to the egg yolk shape detection in the experiment. Compared with the existing typical DBN network model, the recognition accuracy of LQ_DBN model in egg yolk shape detection experiment is higher than that of CC-PSO-DBN, PSO_MDBN and standard DBN models, and the stability of recognition accuracy is the highest among the four comparison models, which shows that the DBN network model based on the improved QPSO algorithm has achieved better optimization effect.