Constructing a reasonable online monitoring model is an important guarantee for real-time control on dam safety.Ai-ming at the problems of conventional LSTM model such as easily affected by multi-parameters combination,weak generalization ability of optimal parameters and difficult manual selection of parameters,the influence of key parameters such as learning rate,block size,maximum number of iterations and number of hidden layer units on the accuracy of dam safety online monitoring model were deeply analyzed.An improved particle swarm optimization algorithm(IPSO)integrating nonlinear inertia weight,shrinkage factor and Cauchy disturbance term was proposed,and the IPSO-LSTM model for dam safety monitoring was constructed by cou-pling with LSTM model.The engineering verification showed that this model can automatically search for the optimal parameters,has high accuracy and strong robustness,and is suitable for dam safety monitoring data sequences of different types and lengths.The error can be reduced by at least 30％compared with the conventional LSTM model with artificial parameters.Relevant experi-ences can provide technical support for online monitoring of dam operation safety.
关键词
大坝安全/监控模型/粒子群优化改进算法(IPSO)/长短时神经网络(LSTM)/自动寻优
Key words
dam safety/monitoring model/improved particle swarm optimization algorithm(IPSO)/long and short time neu-ral network(LSTM)/automatic optimization
维普
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