Nonlinear system identification with continuous piecewise linear neural network

Authors:
Xiaolin Huang;Jun Xu;Shuning Wang
Affiliations:
Department of Automation, Tsinghua University, Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing 100084, PR China;Department of Automation, Tsinghua University, Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing 100084, PR China;Department of Automation, Tsinghua University, Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing 100084, PR China
Venue:
Neurocomputing
Year:
2012

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Abstract

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.