Time Series Forecasting of Mobile Robot Motion Sensors Using LSTM Networks

Abstract

Deep neural networks are a tool for acquiring an approximation of the robot mathematical model without available information about its parameters. This paper compares the LSTM, stacked LSTM and phased LSTM architectures for time series forecasting. In this paper, motion sensor data from mobile robot driving episodes are used as the experimental data. From the experiment, the models show better results for short-term prediction, where the LSTM stacked model slightly outperforms the other two models. Finally, the predicted and actual trajectories of the robot are compared.

Regional Economic Forecasting Method Based on Recurrent Neural Network

“…Gated recurrent units (GRU) is a typical RNN model [29][30][31]; the GRU network model and its structure is shown in Figure 1. Please rephrase the part for clarity, the structure of GRU is similar to the LSTM, but much simpler than the LSTM, which only contains two kinds of gates: the reset gate and the update gate.…”