Nonlinear dynamical system modeling via recurrent neural networks and a weighted state space search algorithm

Leong Kwan Li, Sally Shao, Ka Fai Cedric Yiu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Given a task of tracking a trajectory, a recurrent neural network may be considered as a black-box nonlinear regression model for tracking un-known dynamic systems. An error function is used to measure the difference between the system outputs and the desired trajectory that formulates a non-linear least square problem with dynamical constraints. With the dynamical constraints, classical gradient type methods are dificult and time consuming due to the involving of the computation of the partial derivatives along the trajectory. We develop an alternative learning algorithm, namely the weighted state space search algorithm, which searches the neighborhood of the target trajectory in the state space instead of the parameter space. Since there is no computation of partial derivatives involved, our algorithm is simple and fast. We demonstrate our approach by modeling the short-term foreign exchange rates. The empirical results show that the weighted state space search method is very promising and effective in solving least square problems with dynam-ical constraints. Numerical costs between the gradient method and our the proposed method are provided.
Original languageEnglish
Pages (from-to)385-400
Number of pages16
JournalJournal of Industrial and Management Optimization
Volume7
Issue number2
DOIs
Publication statusPublished - 1 May 2011

Keywords

  • Nonlinear dynamical system
  • Recurrent neural network
  • State space search

ASJC Scopus subject areas

  • Business and International Management
  • Strategy and Management
  • Control and Optimization
  • Applied Mathematics

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