Abstract
Identification of nonlinear system could help to understand and model the internal mechanism of real complex systems. In this paper, complex-valued version of flexible neural tree (CVFNT) model is proposed to identify nonlinear systems. In order to search the optimal structure and parameters of CVFNT model, a new hybrid evolutionary method based on structure-based evolutionary algorithm and firefly algorithm is employed. Two nonlinear system identification experiments are used to test CVFNT model. The results reveal that CVFNT model performs better than the proposed real-valued neural networks.
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Acknowledgement
This work was supported by the PhD research startup foundation of Zaozhuang University (No. 2014BS13), Zaozhuang University Foundation (No. 2015YY02), and Shandong Provincial Natural Science Foundation, China (No. ZR2015PF007).
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Jia, L., Zhang, W., Yang, B. (2017). Identification of Nonlinear System Based on Complex-Valued Flexible Neural Network. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_18
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DOI: https://doi.org/10.1007/978-3-319-68935-7_18
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