Abstract
Surface electromyography signal is non-stationary, susceptible to external interference. For this situation under this case, cyclostationary input with the inverse nonlinear mapping of the Hammerstein-Wiener model were combined to build surface electromyography model and to realize the blind discrete nonlinear system identification. The parameters of model were used as input of improved BP neural network. The experiments results demonstrated the effectiveness of this approach.
This paper is supported by the Key Project of Science and Technology Development Plan for Jilin Province (Grant No.20090350), Chinese College Doctor Special Scientific Research Fund (Grant No.20100061110029) and the Jilin University "985 project" Engineering Bionic Sci. & Tech. Innovation Platform.
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Li, Y., Tian, Y., Shang, X., Chen, W. (2011). Modeling and Classification of sEMG Based on Blind Identification Theory. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21111-9_38
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DOI: https://doi.org/10.1007/978-3-642-21111-9_38
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