Study on Extreme learning machine optimized of the fireworks algorithm
[1]Wang Jiechang,Qun Kang,Peng Jinzhu.Study on Extreme learning machine optimized of the fireworks algorithm[J].Journal of Zhengzhou University (Engineering Science),2016,37(02):20-24.[doi:10.3969/j.issn.1671-6833.201505001]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
37卷
Number of periods:
2016 02
Page number:
20-24
Column:
Public date:
2016-04-18
- Title:
- Study on Extreme learning machine optimized of the fireworks algorithm
- Keywords:
- fireworks algorithm; ELM; test error; node in hidden layer; FWAELMfitting
- CLC:
- -
- DOI:
- 10.3969/j.issn.1671-6833.201505001
- Abstract:
- Use the fireworks algorithm (fireworks algorithm, FWA) to optimize the extreme learning machine (extreme learning machine, ELM). First, the fireworks algorithm undergoes multiple iterations to determine M optimal fireworks, and the RMSE of the extreme learning machine test sample is used as the fireworks algorithm The fitness function of each iteration achieves the effect of optimizing the input weight matrix and hidden layer deviation of the extreme learning machine. Finally, the output matrix is obtained according to the generalized inverse. The test results of the one-dimensional sinC function show that the fireworks algorithm optimizes the limit The learning machine can achieve higher accuracy with fewer nodes in the hidden layer, which is 29.58% lower than the test error of the extreme learning machine. On the basis of the above, the fitting experiment of the Gaussian normal distribution function was done to verify that The fireworks algorithm optimizes the extreme learning machine to have better fitting performance than the extreme learning machine.
- References:
- -
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