Abstract
A recursive method of decreased extreme learning machine (DELM) is proposed for online identification of nonlinear systems. The output weights of ELM can be recursively updated by decreasing the hidden nodes one by one in an efficient manner. Furthermore, a ridge parameter is introduced into the transposed matrix to overcome the singular problem. The simulation results for several benchmark problems demonstrate that the proposed DELM method can reduce the computational complexity efficiently, and maintain the good prediction performance of the model, compared to the traditional ELM algorithm.
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Acknowledgments
The authors would like to gratefully acknowledge National Natural Science Foundation of China (Grant Nos. 61004136 and 61273069) for the financial support.
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Zhang, M., Liu, Y., Gao, Z. (2013). A Decreased Extreme Learning Machine with Ridge Parameter for Online Identification of Nonlinear Systems. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38524-7_45
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DOI: https://doi.org/10.1007/978-3-642-38524-7_45
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