Adaptive neural-network predictive control for nonminimum-phase nonlinear processes

Wei Wu, Wei Ching Hsu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


An adaptive neural-network predictive control strategy for a class of nonlinear processes, which exhibit input multiplicities and change in the sign of steady-state gains, is presented. According to the graphic-based determination associated with prescribed input/output patterns, the feed-forward neural network (FNN) is attributed to reconstruct dynamic and steady-state characteristics of minimum-phase modes with specified operating ranges. The flexible predictive control strategy using on-line neuro-based adaptation is developed for enhancing the predictive capability of neural network. Finally, the proposed FNN-based implementation is illustrated on simulations of both isothermal and adiabatic CSTR systems.

Original languageEnglish
Pages (from-to)177-193
Number of pages17
JournalChemical Engineering Communications
Issue number2
Publication statusPublished - 2007 Feb

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)


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