Hidden Markov Mixtures of Experts for prediction of non-stationary dynamics

Stefan Liehr, Klaus Pawelzik, Jens Kohlmorgen, Steven Lemm, Klaus Robert Mueller

Research output: Contribution to conferencePaper

5 Citations (Scopus)

Abstract

The prediction of non-stationary dynamical systems may be performed by identifying appropriate sub-dynamics and an early detection of mode changes. In this paper, we present a framework which unifies the mixtures of experts approach and a generalized hidden Markov model with an input-dependent transition matrix: the Hidden Markov Mixtures of Experts (HMME). The gating procedure incorporates state memory, information about the current location in phase space, and the previous prediction performance. The experts and the hidden Markov gating model are simultaneously trained by an EM algorithm that maximizes the likelihood during an annealing procedure. The HMME architecture allows for a fast on-line detection of mode changes: change points are detected as soon as the incoming input data stream contains sufficient information to indicate a change in the dynamics.

Original languageEnglish
Pages195-204
Number of pages10
Publication statusPublished - 1999
Externally publishedYes
EventProceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99) - Madison, WI, USA
Duration: 1999 Aug 231999 Aug 25

Other

OtherProceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99)
CityMadison, WI, USA
Period99/8/2399/8/25

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Hidden Markov Mixtures of Experts for prediction of non-stationary dynamics'. Together they form a unique fingerprint.

  • Cite this

    Liehr, S., Pawelzik, K., Kohlmorgen, J., Lemm, S., & Mueller, K. R. (1999). Hidden Markov Mixtures of Experts for prediction of non-stationary dynamics. 195-204. Paper presented at Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99), Madison, WI, USA, .