Deep reinforcement learning in production systems: a systematic literature review
Shortening product development cycles and fully customisable products pose major
challenges for production systems. These not only have to cope with an increased product …
challenges for production systems. These not only have to cope with an increased product …
Deep reinforcement learning with shallow controllers: An experimental application to PID tuning
NP Lawrence, MG Forbes, PD Loewen… - Control Engineering …, 2022 - Elsevier
Deep reinforcement learning (RL) is an optimization-driven framework for producing control
strategies for general dynamical systems without explicit reliance on process models. Good …
strategies for general dynamical systems without explicit reliance on process models. Good …
A survey on artificial neural networks application for identification and control in environmental engineering: Biological and chemical systems with uncertain models
Artificial neural networks (ANNs) are considered efficient tools for modeling complex, non-
linear processes with uncertain dynamic models. ANNs were originally applied as effective …
linear processes with uncertain dynamic models. ANNs were originally applied as effective …
A novel time series forecasting model with deep learning
Z Shen, Y Zhang, J Lu, J Xu, G Xiao - Neurocomputing, 2020 - Elsevier
Time series forecasting is emerging as one of the most important branches of big data
analysis. However, traditional time series forecasting models can not effectively extract good …
analysis. However, traditional time series forecasting models can not effectively extract good …
Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion
Abstract Nowadays, Artificial intelligence (AI), combined with the digitalization of healthcare,
can lead to substantial improvements in Patient Care, Disease Management, Hospital …
can lead to substantial improvements in Patient Care, Disease Management, Hospital …
A reinforcement learning-based multi-agent framework applied for solving routing and scheduling problems
This article presents a multi-agent framework for optimization using metaheuristics, called
AMAM. In this proposal, each agent acts independently in the search space of a …
AMAM. In this proposal, each agent acts independently in the search space of a …
Implementation of PID controller for liquid level system using mGWO and integration of IoT application
Industrial automation is a multi-disciplinary study field that is constantly evolving. This
research proposes IoT-based real-time liquid level monitoring and control in a single tank …
research proposes IoT-based real-time liquid level monitoring and control in a single tank …
Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings
The heating, ventilating and air conditioning (HVAC) systems energy demand can be
reduced by manipulating indoor conditions within the comfort range, which relates to control …
reduced by manipulating indoor conditions within the comfort range, which relates to control …
Meta-reinforcement learning for the tuning of PI controllers: An offline approach
DG McClement, NP Lawrence, JU Backström… - Journal of Process …, 2022 - Elsevier
Meta-learning is a branch of machine learning which trains neural network models to
synthesize a wide variety of data in order to rapidly solve new problems. In process control …
synthesize a wide variety of data in order to rapidly solve new problems. In process control …
Algorithm for autonomous power-increase operation using deep reinforcement learning and a rule-based system
The power start-up operation of a nuclear power plant (NPP) increases the reactor power to
the full-power condition for electricity generation. Compared to full-power operation, the …
the full-power condition for electricity generation. Compared to full-power operation, the …