Asset Details
Do you wish to reserve the book?
A flexible linear temporal logic-based data inference architecture for industrial process prediction
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A flexible linear temporal logic-based data inference architecture for industrial process prediction
Do you wish to request the book?
A flexible linear temporal logic-based data inference architecture for industrial process prediction
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
A flexible linear temporal logic-based data inference architecture for industrial process prediction
2026
Overview
Artificial neural networks, particularly recurrent architectures like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), are widely used for time series prediction. However, achieving high accuracy, especially for complex industrial process data, remains challenging, as standard models may not explicitly capture local temporal trends effectively. This paper proposes a novel data inference structure based on Propositional Linear Temporal Logic (PLTL) designed to capture qualitative data trends within a sliding window. This PLTL inference structure is integrated into LSTM and GRU networks, creating two new models: L-LSTM and L-GRU. The PLTL module provides an interpretable, logic-based representation of recent data dynamics, which modulates the standard recurrent computations, enabling the networks to learn temporal dependencies more effectively, as demonstrated by empirical results. The proposed methods are evaluated on the TAIEX financial benchmark dataset and real-world data from a textile manufacturing process. Experimental results, evaluated using Root Mean Square Error (RMSE), indicate that the L-LSTM and L-GRU models demonstrate statistically significant improved prediction accuracy compared to baseline LSTM, GRU, Deep Belief Networks (DBN), and a Fuzzy Time Series-LSTM (FTS-LSTM) hybrid model on the benchmark dataset, and show strong performance on the industrial data.
Publisher
Springer International Publishing,Springer Nature B.V,Springer
Subject
