
Research Article
Dynamic Adaptive Search Strategy Based Incremental Extreme Learning Machine Based on
@INPROCEEDINGS{10.1007/978-3-030-72792-5_44, author={Zuozhi Liu and Jianjun Jiao and Quan Yuan}, title={Dynamic Adaptive Search Strategy Based Incremental Extreme Learning Machine Based on}, proceedings={Simulation Tools and Techniques. 12th EAI International Conference, SIMUtools 2020, Guiyang, China, August 28-29, 2020, Proceedings, Part I}, proceedings_a={SIMUTOOLS}, year={2021}, month={4}, keywords={Single-hidden layer feedforward network Incremental extreme learning machine Enhanced grey wolf optimization Universal approximation}, doi={10.1007/978-3-030-72792-5_44} }
- Zuozhi Liu
Jianjun Jiao
Quan Yuan
Year: 2021
Dynamic Adaptive Search Strategy Based Incremental Extreme Learning Machine Based on
SIMUTOOLS
Springer
DOI: 10.1007/978-3-030-72792-5_44
Abstract
Extreme learning machine (ELM) is a promising method for the learning of single-hidden layer feedforward network (SLFN) which is attractive for its simplicity and high efficiency. However, during the rapid development of ELM algorithm, the determination of suitable network architecture is still a challenging work. To deal with this issue, this work develops a modified ELM algorithm based on a novel adaptive optimization method. Specifically, we use the growth structure strategy to design the network architecture. During the learning process of the proposed algorithm, the grey wolf optimization (GWO) technique is then introduced to seek the optimal parameters for hidden nodes instead of random selection. In addition, to improve the convergence speed, we further ameliorate the traditional GWO approach. Experiment results over some benchmark applications indicate that our AI-ELM algorithm can dramatically reduce the scale of network and obtains the better generalization performance than other classical ELM algorithms.