系统仿真学报 ›› 2020, Vol. 32 ›› Issue (1): 1-8.doi: 10.16182/j.issn1004731x.joss.17-0461

• 仿真建模理论与方法 •    下一篇

基于曲率信息的改进BP算法及其在FNN中的应用

熊伟丽1,2, 孙文心1, 史旭东1   

  1. 1. 江南大学 物联网工程学院,江苏 无锡 214122;
    2. 江南大学 轻工过程先进控制教育部重点实验室,江苏 无锡 214122
  • 收稿日期:2017-11-08 修回日期:2018-01-18 发布日期:2020-01-17
  • 作者简介:熊伟丽(1978-),女,河南洛阳,博士,教授,硕导,研究方向为复杂工业过程建模及优化,智能优化算法及应用;孙文心(1993-),男,江苏无锡,硕士生,研究方向为复杂工业过程建模及优化。
  • 基金资助:
    国家自然科学基金(61773182)

Curvature-based BP Algorithm Optimization and Its Application in FNN

Xiong Weili1,2, Sun Wenxin1, Shi Xudong1   

  1. 1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
    2. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
  • Received:2017-11-08 Revised:2018-01-18 Published:2020-01-17

摘要: 针对步长选取影响误差反向传播(BP,Back Propagation)算法优化效率问题,提出一种基于曲率信息的步长优化BP算法,并将其应用到了模糊神经网络(FNN)的训练过程中。参考牛顿法的思想,根据代价函数的梯度及梯度方向上的曲率信息来确定模型参数调整的方向和幅度。仅需考虑梯度方向上的二阶信息,因此不需要存储和处理Hessian矩阵。通过一个数值仿真和高炉炼铁过程数据建模实验,验证了方法的有效性及训练效率。

关键词: 步长优化, BP算法, 模糊神经网络, 学习速度

Abstract: In order to improve the optimization efficiency of BP algorithm affected by the selection of step size, a step size optimization BP algorithm based on curvature information is proposed and applied to the training process of FNN (Fuzzy Neural Network). Reference to Newton's method, The gradient of the cost function and the curvature information in the direction are calculated to determine the direction and magnitude of the parameter adjustment in each iteration. This method only needs to consider the two order information of the gradient direction, so it does not need the storage and processing of Hessian matrix. The effectiveness and efficiency of the proposed method are verified by a numerical simulation and data simulation of blast furnace ironmaking process.

Key words: Step size optimization, BP algorithm, Fuzzy neural network, Learning speed

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