在线自适应神经网络算法及参数鲁棒性分析

王涛1,2,3,翟绪恒2,孟丽岩2

振动与冲击 ›› 2019, Vol. 38 ›› Issue (8) : 210-217.

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振动与冲击 ›› 2019, Vol. 38 ›› Issue (8) : 210-217.
论文

在线自适应神经网络算法及参数鲁棒性分析

  • 王涛1,2,3,翟绪恒2,孟丽岩2
作者信息 +

An online adaptive neural network algorithm and its parameters robustness analysis

  • WANG Tao1,2,3,ZHAI Xuheng2,MENG Liyan2
Author information +
文章历史 +

摘要

为了提高传统BP神经网络在线预测精度和计算效率,提出一种在线自适应神经网络算法。该算法在传统BP网络的输入层和隐含层之间增加一个反馈层,通过存储内部状态增强网络动态映射能力,以提高算法自适应性。同时,在学习阶段采用递推形式在线训练算法权值和阈值,以提高算法计算精度和计算效率。然后,基于两组防屈曲支撑构件拟静力试验数据,在线预测防屈曲支撑恢复力。研究表明:与传统神经网络算法相比,本文所提出了在线自适应网络算法具有更好的在线预测精度和计算效率。最后,对网络结构中的输入变量、输入和观测样本、隐含层激活函数等算法参数进行了鲁棒性分析,找到了算法参数对算法性能的影响规律,给出算法应用时参数选择建议。

Abstract

In order to improve the on-line prediction accuracy and computational efficiency of the traditional BP neural network, a novel online adaptive neural network algorithm was put forward.A feedback connection layer between the input layer and hidden layer in the proposed algorithm was added on the basis of the BP network to improve the adaptiveness of the algorithm by storing internal state information which can enhance the dynamic mapping capability of network.Meanwhile, weights and thresholds were online trained using the recursive form in the learning phase to improve the calculation precision and calculation efficiency.Then, restoring forces of the buckling-restrained brace (BRB) were online predicted based on two groups of BRBs pseudo-static test data.Results show that the proposed online adaptive neural network algorithm has better on-line prediction accuracy and computational efficiency compared with the traditional BP algorithm.Finally, the robustness of algorithm parameters including the input variable, samples of input and observation and activation function of the hidden layer in the network structure were analyzed.The influence rules of algorithm parameters on the algorithm performance were revealed and the parameter selections suggestions in the algorithm application were given.

关键词

在线预测 / 神经网络算法 / 恢复力模型 / 鲁棒性分析 / 防屈曲支撑

Key words

In order to improve the on-line prediction accuracy and computational efficiency of the traditional BP neural network, a novel online adaptive neural network algorithm was put forward. A feedback connection layer between the input layer and hidden layer in the proposed algorithm was added on the basis of the BP network to improve the adaptiveness of the algorithm by storing internal state information which can enhance the dynamic mapping capability of network. Meanwhile, weights and thresholds were online trained using the recursive form in the learning phase to improve the calculation precision and calculation efficiency. Then, restoring forces of the buckling-restrained brace (BRB) were online predicted based on two groups of BRBs pseudo-static test data. Results show that the proposed online adaptive neural network algorithm has better on-line prediction accuracy and computational efficiency compared with the traditional BP algorithm. Finally, robustness of algorithm parameters including the input variable, samples of input and observation and activation function of the hidden layer in the network structure were analyzed. The influence rules of algorithm parameters on the algorithm performance are revealed and the parameter selections suggestions in the algorithm application are given.
On-line prediction
/ neural network algorithm / restoring force model / robustness analysis / buckling-restrained brace

引用本文

导出引用
王涛1,2,3,翟绪恒2,孟丽岩2. 在线自适应神经网络算法及参数鲁棒性分析[J]. 振动与冲击, 2019, 38(8): 210-217
WANG Tao1,2,3,ZHAI Xuheng2,MENG Liyan2. An online adaptive neural network algorithm and its parameters robustness analysis[J]. Journal of Vibration and Shock, 2019, 38(8): 210-217

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