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 conferencePaperpeer-review

    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
    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

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