A Transfer Learning LSTM Network-Based Severity Evaluation for Intermittent Faults of an Electrical Connector

Shi, Junyou; He, Qingjie; Wang, Zili

doi:10.1109/tcpmt.2020.3043011

Cited by 9 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the basis of recurrent neural network (RNN), each ordinary node in the hidden layer is replaced by a memory cell with input gate, forget gate, and output gate. LSTM network can balance the weight between history input information and current input information through the calculations of input gate, forget gate, and output gate and the update of the memory cell state (Shi et al, 2021 ). As shown in Figure 3 , the regression model of the LSTM network is constructed with an input layer with four nodes, and two LSTM layers employed to extract road adhesion coefficient feature, a full connected layer, and a regression layer with four nodes.…”

Section: A Normalization With a Fomvgm And A Lstm Networkmentioning

confidence: 99%

A road adhesion coefficient-tire cornering stiffness normalization method combining a fractional-order multi-variable gray model with a LSTM network and vehicle direct yaw-moment robust control

et al. 2023

View full text Add to dashboard Cite

A normalization method of road adhesion coefficient and tire cornering stiffness is proposed to provide the significant information for vehicle direct yaw-moment control (DYC) system design. This method is carried out based on a fractional-order multi-variable gray model (FOMVGM) and a long short-term memory (LSTM) network. A FOMVGM is used to generate training data and testing data for LSTM network, and LSTM network is employed to predict tire cornering stiffness with road adhesion coefficient. In addition to that, tire cornering stiffness represented by road adhesion coefficient can be used to built vehicle lateral dynamic model and participate in DYC robust controller design. Simulations under different driving cycles are carried out to demonstrate the feasibility and effectiveness of the proposed normalization method of road adhesion coefficient and tire cornering stiffness and vehicle DYC robust control system, respectively.

show abstract

Section: A Normalization With a Fomvgm And A Lstm Networkmentioning

confidence: 99%