The 'moving targets' training algorithm

Richard Rohwer, J. Kindermann (Editor), A. Linden (Editor)

    Research output: Unpublished contribution to conferenceUnpublished Conference Paperpeer-review


    A simple method for training the dynamical behavior of a neural network is derived. It is applicable to any training problem in discrete-time networks with arbitrary feedback. The method resembles back-propagation in that it is a least-squares, gradient-based optimization method, but the optimization is carried out in the hidden part of state space instead of weight space. A straightforward adaptation of this method to feedforward networks offers an alternative to training by conventional back-propagation. Computational results are presented for simple dynamical training problems, with varied success. The failures appear to arise when the method converges to a chaotic attractor. A patch-up for this problem is proposed. The patch-up involves a technique for implementing inequality constraints which may be of interest in its own right.
    Original languageEnglish
    Publication statusUnpublished - 1990
    EventDistributed Adaptive Information Processing (DANIP) -
    Duration: 1 Jan 19901 Jan 1990


    OtherDistributed Adaptive Information Processing (DANIP)

    Bibliographical note

    Figures unavailable electronically


    • dynamical behavior
    • neural network
    • networks
    • back-propagation


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