改进型粒子群优化算法的BP神经网络全息图压缩

针对拥有庞大数据量的全息图再现像质量不理想的问题, 提出一种针对粒子群优化算法(PSO: Particle Swarm Optimization)中学习因子和惯性权值进行动态调整的方法, 将改进后的算法与反向传播(BP: Back Propagation)神经网络相融合形成改进型粒子群优化BP 神经网络(MPSO-BP: Modified Particle Swarm Optimizing
BP Neural Network)并用于全息图压缩。通过与BP 神经网络和粒子群优化BP 神经网络(PSO-BP: Particle Swarm Optimizing BP Neural Network)压缩算法进行对比, 证明了该网络压缩算法在保持较好的压缩效率时得到的全息图再现像质量更好。

Hologram Compression Using BP Neural Network by Modified Particle Swarm Optimization

To solve the problem that hologram contains a large amount of data and performance of reproduced hologram is not ideal. A method is proposed to adjust dynamically learning factor and inertia weight for the PSO (Particle Swarm Optimization). The MPSO-BP(Modified Particle Swarm Optimizing BP Neural Network) is constructed combining the modified algorithm with BP(Back Propagation) neural network. Comparing with the BP neural network compression and PSO-BP(Particle Swarm Optimizing BP Neural Network) compression, the network has the advantages of better performance of reproducing hologram while maintaining a good compression efficiency.