Stacked bidirectional LSTM RNN to evaluate the remaining useful life of supercapacitor

To predict the remaining useful life of supercapacitor, a data-based model is established by using a stacked bidirectional long short-term memory recurrent neural network. On the basis of the traditional long short-term memory recurrent neural network, a reverse recurrent layer with t time and subsequent time values in the input sequence is added. A stacked network can ensure enough capacity space. Simulation results show that the network has superior performance when the number of hidden layers is 2, the predicted RMSE and MAE are 0.0275 and 0.0241, respectively. Meanwhile, simulation compares ordinary and bidirectional recurrent neural networks and the bidirectional recurrent neural networks with different recurrent units. For subsequent ameliorate, this project will add swarm intelligence algorithm to optimize the initial weight of neural network and reduce the initial prediction error.