摘要
提出基于萤火虫算法与LM(Levenberg-Marquardt)算法相结合的FA-LMBP混合神经网络算法图像压缩模型。利用该模型进行求解时,通过萤火虫算法按照目标函数进行全局搜索,得到反向传播(BP)神经网络的一组权阈值最优近似解,以该近似解作为BP模型初值,利用LM算法对这组权阈值进行二次优化训练,得到最终的图像压缩模型。实验结果表明,在相同训练次数和相同误差精度下,基于FA-LMBP混合神经网络算法的压缩图像模型重建质量明显高于BP算法和LMBP算法模型。
An image compression model based on the FA-LMBP hybrid neural network algorithm is proposed by the combination of the firefly algorithm and LM(Levenberg-Marquardt)algorithm.First,a set of optimal approximate solutions of the backpropagation(BP)network weight threshold are obtained by the global search of the target function based on the firefly algorithm,then the approximate solution is used as the initial value of the BP model,and the LM algorithm is used to carry out the secondary optimization training for these weight thresholds,thereby obtaining the final image compression model.The experimental results show that the reconstruction quality of the compression image model based on the FA-LMBP hybrid neural network algorithm is obviously higher than that of the basic BP model and the LMBP model under the same training times and error accuracy.
作者
王海军
金涛
门克内木乐
Wang Haijun;Jin Tao;Men Ke Neimule(Department of Mathematics and Computer Engineering,Ordos Institute of Technology,Ordos,Inner Mongolia 017000,China;Department of Information Engineering,Ordos Institute of Technology,Ordos,Inner Mongolia 017000,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2019年第19期119-125,共7页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61741509,61205127,61167004)
内蒙古自治区高等学校科学研究项目(NJZY19260)
作者简介
王海军,E-mail:wanghaijun11249@126.com。
