| 基于新的改进粒子群算法的BP神经网络在拟合非线性函数中的应用 |
| |
| 引用本文： | 林宇锋,邓洪敏,史兴宇.基于新的改进粒子群算法的BP神经网络在拟合非线性函数中的应用[J].计算机科学,2017,44(Z11):51-54. |
| |
| 作者姓名： | 林宇锋 邓洪敏 史兴宇 |
| |
| 作者单位： | 四川大学电子信息学院 成都610065,四川大学电子信息学院 成都610065,四川大学电子信息学院 成都610065 |
| |
| 基金项目： | 本文受国家自然科学基金(61174025)资助 |
| |
| 摘 要： | 介绍了一种基于新的改进粒子群算法(NIPSO)的BP神经网络来解决拟合非线性函数所出现的误差较大的问题。此算法在粒子群优化算法基础上,分别让权重和学习因子非线性和线性变化,建立基于新的粒子群优化算法的新模型,再与BP神经网络结合之后来拟合非线性函数。结果表明,新的改进粒子群优化算法更加合理且高效地提高了BP神经网络的拟合能力,减小了拟合误差,提高了拟合精度。
|
| 关 键 词： | BP神经网络 粒子群优化算法 函数拟合 |
Application of BP Neural Network Based on Newly Improved Particle Swarm Optimization Algorithm in Fitting Nonlinear Function |
| |
| LIN Yu-feng,DENG Hong-min and SHI Xing-yu.Application of BP Neural Network Based on Newly Improved Particle Swarm Optimization Algorithm in Fitting Nonlinear Function[J].Computer Science,2017,44(Z11):51-54. |
| |
| Authors: | LIN Yu-feng DENG Hong-min and SHI Xing-yu |
| |
| Affiliation: | College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China,College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China and College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China |
| |
| Abstract: | A newly improved particle swarm optimization algorithm for the optimization of BP neural network was introduced to solve the problem of large error in fitting the nonlinear function.In this algorithm,a new model based on the newly improved particle swarm optimization algorithm is established through respectively changing the weight non-li-nearly and learning factor linearly,then it is applied to non-linear function fitting by combining with BP neural network.The results show that the newly improved particle swarm optimization algorithm can more rationally and effectively boost the fitting ability of BP neural network,and improve the accuracy of the fitting. |
| |
| Keywords: | BP neural network Particle swarm optimization algorithm Function fitting |
|
| 点击此处可从《计算机科学》浏览原始摘要信息 |
|
点击此处可从《计算机科学》下载全文 |
|
