Stacked encoded cascade error feedback deep extreme learning machine network for manufacturing order completion time

In this paper, a novel stacked encoded cascade error feedback deep extreme learning machine (SEC-E-DELM) network is proposed to predict order completion time (OCT) considering the historical production planning and control data. Usually, the actual OCT significantly deviates from the planned because...

Full description

Saved in:
Bibliographic Details
Published in:Journal of intelligent manufacturing Vol. 36; no. 2; pp. 1313 - 1339
Main Authors: Khan, Waqar Ahmed, Masoud, Mahmoud, Eltoukhy, Abdelrahman E. E., Ullah, Mehran
Format: Journal Article
Language:English
Published: New York Springer US 01.02.2025
Springer Nature B.V
Subjects:
Business and Management
Coders
Coding
Comparative studies
Completion time
Control
Control data (computers)
Disturbances
Error feedback
Machine learning
Machines
Manufacturing
Mechatronics
Neural networks
Processes
Production
Production planning
Robotics
Deep learning
Order completion time
Network weights
Extreme learning machine
Autoencoder
ISSN:0956-5515, 1572-8145
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, a novel stacked encoded cascade error feedback deep extreme learning machine (SEC-E-DELM) network is proposed to predict order completion time (OCT) considering the historical production planning and control data. Usually, the actual OCT significantly deviates from the planned because of recessive disturbances. The disturbances do not shut down production but slow down the production that accumulates over time, causing significant deviation of actual time from planned. The generation of weight parameters in neural networks using a randomization approach has a significant effect on generalization performance. To predict the OCT, firstly, the stacked autoencoder is used to generate input connection weights for the network by learning a deep representation of the real data. Secondly, the learned distribution of the encoder is connected to the network output through output connection weights incrementally learned by the Moore–Penrose inverse. Thirdly, the new hidden unit is added one by one to the network, which receives input connections from the input units and the last layer of the encoder to avoid overfitting and improve model generalization. The input connection weights for the newly added hidden unit are analytically calculated by the error feedback function to enhance the convergence rate by further learning deep features. Lastly, the hidden unit keeps on adding one by one by receiving connections from input units and some of the existing hidden units to make a deep cascade architecture. An extensive comparative study demonstrates that calculating connection weights by the proposed method helps to significantly improve the generalization performance and robustness of the network.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-023-02303-0