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\iteman{io-port 06017374}
\itemau{Huang, Xiaolin; Xu, Jun; Wang, Shuning}
\itemti{Nonlinear system identification with continuous piecewise linear neural network.}
\itemso{Neurocomputing 77, 167-177 (2012).}
\itemab
Summary: This paper considers system identification using domain partition based continuous piecewise linear neural network (DP-CPLNN), which is newly proposed. DP-CPLNN has the capability of representing any continuous piecewise linear (CPWL) function, hence its identification performance can be expected. Another attractive feature of DP-CPLNN is the geometrical property of its parameters. Applying this property, this paper proposes an identification method including domain partition and parameter training. In numerical experiments, DP-CPLNN with this method outperforms hinging hyperplanes and high-level canonical piecewise linear representation, which are two widely used CPWL models, showing the flexibility of DP-CPLNN and the effectiveness of the proposed algorithm in nonlinear identification.
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\itemut{piecewise linear neural network; domain partition; nonlinear system identification}
\itemli{doi:10.1016/j.neucom.2011.09.001}
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