Article: Recurrent neural network optimisation based on linearly constrained numerical methods Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.21 pp.70 - 86 Abstract: Time-series data analysis has grown even more crucial in many sectors as information technology and big data expand rapidly. This work proposes a recurrent neural network (RNN) optimisation model based on the linear constraint numerical method, namely, LSTM-LP optimiser, which combines the powerful time-series modelling capability of long short-term memory (LSTM) and the optimisation characteristics of linear programming (LP) optimisation features, and so effectively improves the training efficiency and stability of the model in resource-constrained environments. This helps to efficiently capture the temporal dependencies in time-series data and solve the noise and missing problems in the data. On two datasets, experimental results show the LSTM-LP optimiser beats the conventional model in several performance criteria. Future studies will investigate more effective optimisation techniques, increase the generalisation capacity of the model, and simplify the hyperparameter tweaking process to thus further promote the model in practical uses. Inderscience Publishers - linking academia, business and industry through research

Title: Recurrent neural network optimisation based on linearly constrained numerical methods

Authors: Wenmin Song; Wei Han; Ping Gu; Min Li

Addresses: Department of Artificial Intelligence, Laiwu Vocational and Technical College, Laiwu 271199, China ' Department of Artificial Intelligence, Laiwu Vocational and Technical College, Laiwu 271199, China ' Department of Artificial Intelligence, Laiwu Vocational and Technical College, Laiwu 271199, China ' Department of Artificial Intelligence, Laiwu Vocational and Technical College, Laiwu 271199, China

Abstract: Time-series data analysis has grown even more crucial in many sectors as information technology and big data expand rapidly. This work proposes a recurrent neural network (RNN) optimisation model based on the linear constraint numerical method, namely, LSTM-LP optimiser, which combines the powerful time-series modelling capability of long short-term memory (LSTM) and the optimisation characteristics of linear programming (LP) optimisation features, and so effectively improves the training efficiency and stability of the model in resource-constrained environments. This helps to efficiently capture the temporal dependencies in time-series data and solve the noise and missing problems in the data. On two datasets, experimental results show the LSTM-LP optimiser beats the conventional model in several performance criteria. Future studies will investigate more effective optimisation techniques, increase the generalisation capacity of the model, and simplify the hyperparameter tweaking process to thus further promote the model in practical uses.

Keywords: time-series data analysis; recurrent neural network; RNN; linear programming; LP; long short-term memory; LSTM; resource-constrained optimisation.

DOI: 10.1504/IJICT.2025.146834

International Journal of Information and Communication Technology, 2025 Vol.26 No.21, pp.70 - 86

Received: 15 Apr 2025
Accepted: 29 Apr 2025
Published online: 20 Jun 2025 *

Title: Recurrent neural network optimisation based on linearly constrained numerical methods

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