计算机工程与应用2018，Vol.54Issue(3)：50-56,130,8.DOI:10.3778/j.issn.1002-8331.1609-0179
基于自适应花授粉算法的BP神经网络结构优化
New BP neural network based on adaptive flower pollination algorithm
摘要
Abstract
The standard BP neural networks adjusts the weights and threshold value by the gradient descent method, which is easy to fall into the local optimum and slow convergence rate. Thus, the application of BP neural network is limited. In this paper, a Self-adaptive Flower Pollination Algorithm(SFPA) is proposed and used to optimize the weights and threshold value of BP neural networks. Firstly, the self-adaptive SFPA is put forward by adjusting the switch probability of FPA during iterations and as in the mutation operator. Then, the SFPA is integrated with the BP in two different forms:SFPA1-BP and SFPA2-BP. Finally, the performance of the new BP network is tested by using function approximation experiments and Iris classification data set. The study shows that SFPA1-BP and SFPA2-BP are superior to other networks, especially in the function approximations and classification.关键词
BP神经网络/花授粉算法/转换概率/变异因子Key words
Back-Propagation neural network/Flower Pollination Algorithm(FPA)/switch probability/mutation operator分类
信息技术与安全科学引用本文复制引用
卞京红,贺兴时,范钦伟,伊宝民..基于自适应花授粉算法的BP神经网络结构优化[J].计算机工程与应用,2018,54(3):50-56,130,8.基金项目
陕西省自然科学基础研究计划项目(No.2014JM100) （No.2014JM100）
陕西省自然软科学研究计划项目(No.2014KRM2801) （No.2014KRM2801）
陕西省教育厅专项科研计划项目(No.14JK1282) （No.14JK1282）
陕西省教育厅专项科研计划项目(No.16JK1341). （No.16JK1341）
