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
An online adaptive neural network algorithm and its parameters robustness analysis
WANG Tao1,2,3，ZHAI Xuheng2，MENG Liyan2
1.Institute of Engineering Mechanics, China Earthquake Administration Key Laboratory of Earthquake Engineering and Engineering Vibration of China Earthquake Administration, Harbin 150080, China;
2. School of Civil Engineering, Heilongjiang University of Science ＆ Technology, Harbin 150022, China;
3. State Key Laboratory of Reduction in Civil Engineering, Tongji University, Shanghai 200092, China
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.
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. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(8): 210-217.
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