TY  - JOUR
A1  - Khayyam, Hamid
A1  - Jamali, Ali
A1  - Bab-Hadiashar, Alireza
A1  - Esch, Thomas
A1  - Ramakrishna, Seeram
A1  - Jalil, Mahdi
A1  - Naebe, Minoo
T1  - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0
T2  - IEEE Access
N2  - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.
Y1  - 2020
UR  - https://opus.bibliothek.fh-aachen.de/opus4/frontdoor/index/index/docId/9276
SN  - 2169-3536
SP  - 1
EP  - 12
PB  - IEEE
CY  - New York, NY
ER  -