Use separate models for each giving each model time and sensor values. Start with two sensors and run both through the swarming process and let us know what difficulties you run into.
Ian On 13 Aug 2014 03:37, "John Blackburn" <[email protected]> wrote: > Dear All, > > I am a researcher at the National Physical Laboratory, London and am > attempting to use NuPIC to model the strain and temperature variations of a > concrete bridge for anomaly detection. The bridge has 10 temperatures > sensors and 8 "tilt sensors" (basically strain) arranged across it. I have > hourly readings for all of these sensors for a 3 year period. I would like > NuPIC to predict all of these quantities (and keep them separate). Compared > to the "hotgym" example, the difference here is that there are 18 separate > streams of data which would need to be suitably encoded and decoded to make > predictions of each one. I suspect the decoding stage would be most > difficult: from the set of cell activations we need to discover 18 numbers > and keep them separate. The HTM should account for cross correlations > between time series as well as auto-correlations. I would like to consider > +1 and +5 predictions, for example. > > During the course of the experiment, various interventions were carried > out at known times. These include cutting support cables, removing chunks > of concrete and adding heavy weights. The NN should show anomalous > behaviour at the time these interventions were done. The system has been > modelled using an Echo Sensor Network so I want to compare performance of > ESN to HTM. > > So, is this task possible with NuPIC and how might I adjust the encoder, > decoder to deal with multiple streams? > > Many thanks for your help, > > John Blackburn. > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >
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