Perceiving information and extracting insights from data is one of the major challenges in smart manufacturing. Real-time data analytics face several challenges in real-life scenarios, while there is a huge treasure of legacy, enterprise and operational data remaining untouched. The current paper exploits the recent advancements of (deep) machine learning for performing predictive and prescriptive analytics on the basis of enterprise and operational data aiming at supporting the operator on the shopfloor. To do this, it implements algorithms, such as Recurrent Neural Networks for predictive analytics, and Multi-Objective Reinforcement Learning for prescriptive analytics. The proposed approach is demonstrated in a predictive maintenance scenario in steel industry.
Adaptability in non-stationary contexts is a very important property and a constant desire for modern intelligent systems and is usually associated with dynamic system behaviors. In this framework, we present a novel methodology of dynamic resource control and optimization for neurofuzzy inference systems. Our approach involves a neurofuzzy model with structural learning capabilities that adds rule nodes when necessary during the training phase. Sensitivity analysis is then applied to the trained network so as to evaluate the network rules and control their usage in a dynamic manner based on a confidence threshold. Therefore, on one hand, we result in a well-balanced structure with an improved adaptive behavior and, on the other hand, we propose a way to control and restrict the "curse of dimensionality". The experimental results on a number of classification problems prove clearly the strengths and benefits of this approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.