Name
Abstract | ||
|---|---|---|
Extreme learning machine algorithm proposed in recent years has been widely used in many fields due to its fast training speed and good generalization performance. Unlike the traditional neural network, the ELM algorithm greatly improves the training speed by randomly generating the relevant parameters of the input layer and the hidden layer. However, due to the randomly generated parameters, some generated "bad" parameters may be introduced to bring negative effect on the final generalization ability. To overcome such drawback, this paper combines the artificial immune system (AIS) with ELM, namely, AIS-ELM. With the help of AIS's global search and good convergence, the randomly generated parameters of ELM are optimized effectively and efficiently to achieve a better generalization performance. To evaluate the performance of AIS-ELM, this paper compares it with relevant algorithms on several benchmark datasets. The experimental results reveal that our proposed algorithm can always achieve superior performance. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1155/2018/3635845 | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE |
Field | DocType | Volume |
Drawback,Convergence (routing),Artificial immune system,Extreme learning machine,Computer science,Artificial intelligence,Artificial neural network,Machine learning | Journal | 2018 |
ISSN | Citations | PageRank |
1687-5265 | 1 | 0.35 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui-yuan Tian | 1 | 1 | 0.35 |
| Shijian Li | 2 | 1155 | 69.34 |
| Tianqi Wu | 3 | 14 | 1.23 |
| Min Yao | 4 | 2 | 1.77 |