Advance Predictions of critical digressions in a noisy industrial process- performance of Extreme Learning Machines versus Artificial Neural Networks
Sign Up to like & getrecommendations! Published in 2018 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2018.05.017
Abstract: Abstract Manmade continuous-time systems like vehicles, grids and industrial processes are susceptible to adverse digressions in performance which can result in losses to severe breakdowns. Traditionally, emergence of faults in systems was detected by algorithms… read more here.
Keywords: neural networks; learning machines; extreme learning; noisy industrial ... See more keywords
Exploring the Function Space of Deep-Learning Machines
Sign Up to like & getrecommendations! Published in 2018 at "Physical review letters"
DOI: 10.1103/physrevlett.120.248301
Abstract: The function space of deep-learning machines is investigated by studying growth in the entropy of functions of a given error with respect to a reference function, realized by a deep-learning machine. Using physics-inspired methods we… read more here.
Keywords: space deep; learning machines; function space; deep learning ... See more keywords
Dynamic Delay Predictions for Large-Scale Railway Networks: Deep and Shallow Extreme Learning Machines Tuned via Thresholdout
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2017.2693209
Abstract: Current train delay (TD) prediction systems do not take advantage of state-of-the-art tools and techniques for handling and extracting useful and actionable information from the large amount of endogenous (i.e., generated by the railway system… read more here.
Keywords: large scale; learning machines; extreme learning; railway ... See more keywords
Lifelong Bayesian Learning Machines for Streaming Industrial Big Data
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2022.3198833
Abstract: With the advent of the big data era and the timeliness requirements of data processing, a large amount of streaming industrial big data is continuously obtained in real time. Facing this kind of flowing and… read more here.
Keywords: streaming industrial; big data; lifelong bayesian; industrial big ... See more keywords
Seizure Detection Based on Adaptive Feature Extraction by Applying Extreme Learning Machines
Sign Up to like & getrecommendations! Published in 2021 at "Traitement du Signal"
DOI: 10.18280/ts.380210
Abstract: Received: 25 December 2020 Accepted: 12 March 2021 Epilepsy is one of the most common chronic disorder which negatively affects the patients' life. The functionality of the brain can be obtained from brain signals and… read more here.
Keywords: learning machines; seizure detection; extreme learning; based adaptive ... See more keywords
Toward Learning Machines at a Mother and Baby Unit
Sign Up to like & getrecommendations! Published in 2020 at "Frontiers in Psychology"
DOI: 10.3389/fpsyg.2020.567310
Abstract: Agnostic analyses of unique video material from a Mother and Baby Unit were carried out to investigate the usefulness of such analyses to the unit. The goal was to improve outcomes: the health of mothers… read more here.
Keywords: mother baby; learning machines; unit; machines mother ... See more keywords
Feature Selection Methods for Extreme Learning Machines
Sign Up to like & getrecommendations! Published in 2022 at "Axioms"
DOI: 10.3390/axioms11090444
Abstract: Extreme learning machines (ELMs) have gained acceptance owing to their high efficiency and outstanding generalization ability. As a key component of data preprocessing, feature selection methods can decrease the noise or irrelevant data for ELMs.… read more here.
Keywords: feature; extreme learning; selection methods; learning machines ... See more keywords