TY - GEN
T1 - Dense Incremental Extreme Learning Machine with Accelerating Amount and Proportional Integral Differential
AU - Zou, Weidong
AU - Xia, Yuanqing
AU - Qiu, Meikang
AU - Cao, Weipeng
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Incremental Extreme Learning Machine (I-ELM) has been widely concerned in recent years because of its ability to automatically find the best number of hidden layer nodes and non-iterative training mechanism. However, in big data scenarios, I-ELM and its variants face great challenges because the least squares method is used to calculate their output weights in the training process, which is time-consuming and unstable. To alleviate this problem, we propose a novel Dense I-ELM based on the Accelerating Amount and the Proportional Integral Differential techniques (AA-PID-DELM) in this paper. For AA-PID-DELM, the dense connection architecture can exert the maximum utility of each hidden layer node, and the accelerating amount and PID techniques can make the model achieve better generalization ability and stability in big data scenarios. Extensive experimental results on the approximation of 2D nonlinear function and several UCI data-sets have proved the effectiveness of AA-PID-DELM.
AB - Incremental Extreme Learning Machine (I-ELM) has been widely concerned in recent years because of its ability to automatically find the best number of hidden layer nodes and non-iterative training mechanism. However, in big data scenarios, I-ELM and its variants face great challenges because the least squares method is used to calculate their output weights in the training process, which is time-consuming and unstable. To alleviate this problem, we propose a novel Dense I-ELM based on the Accelerating Amount and the Proportional Integral Differential techniques (AA-PID-DELM) in this paper. For AA-PID-DELM, the dense connection architecture can exert the maximum utility of each hidden layer node, and the accelerating amount and PID techniques can make the model achieve better generalization ability and stability in big data scenarios. Extensive experimental results on the approximation of 2D nonlinear function and several UCI data-sets have proved the effectiveness of AA-PID-DELM.
KW - Accelerating amount
KW - Incremental extreme learning machine
KW - Neural network architecture
KW - Proportional integral derivative
UR - http://www.scopus.com/inward/record.url?scp=85113812957&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-82136-4_8
DO - 10.1007/978-3-030-82136-4_8
M3 - Conference contribution
AN - SCOPUS:85113812957
SN - 9783030821357
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 89
EP - 100
BT - Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings
A2 - Qiu, Han
A2 - Zhang, Cheng
A2 - Fei, Zongming
A2 - Qiu, Meikang
A2 - Kung, Sun-Yuan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021
Y2 - 14 August 2021 through 16 August 2021
ER -