Training quasi-ARX neural network model by homotopy approach

Jinglu Hu*, Xibin Lu, Kotaro Hirasawa

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)


Quasi-ARX neural networks (NN) are NN based nonlinear models that not only have linear structures similar to linear ARX models, but also have useful interpretation in part of their parameters. However when applying an ordinary backpropagation (BP) for the training, it has potential risk that the BP algorithm is stuck at a local minimum, which results in a poorly trained model. In this paper, a homotopy continuation method is introduced to improve the convergence performance of BP training. The idea is to start the BP training with the criterion function for linear ARX model, which is gradually deformed first into one for quasi-ARX NN model with linear node functions, and then into the actual one for quasi-ARX NN with sigmoid node functions. By building the deformation into a usual recursive procedure for BP training of quasi-ARX NN model with adaptable node functions so that the proposed homotopy based BP algorithm is able to achieve improved convergence performance without much increase in the computation load. Numerical simulation results show that the proposed homotopy based BP has better performance than an ordinary BP.

Original languageEnglish
Number of pages6
Publication statusPublished - 2004 Dec 1
EventSICE Annual Conference 2004 - Sapporo, Japan
Duration: 2004 Aug 42004 Aug 6


ConferenceSICE Annual Conference 2004


  • Backpropagation algorithm
  • Homotopy continuation method
  • Linear ARX model
  • Local minimum
  • Neural network

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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