Ian, Your 10 points are all spot on, great job on understanding my mess!
I'm not sure if feeding the CLA open-loop (no kind of control on) data would be useful on practice, because in this case you're probably better off with standard MPC, but this is probably the right way to start to tackle this problem. It's actually not that hard to simulate a complicated dynamic system with noise and disturbances and gather "experimental" data. If I have time I will look into this and share my findings here. It'd be cool to see how the CLA would respond to things such as large time constants (slow dynamic response) and/or considerable deadtime (time-delay) before trying to actually control a system with it. The main difference from this to typical CLA applications is that the system inputs are independent and thus their prediction is meaningless. Would this make the prediction of the system outputs (which depend on their past values and on current and past inputs) harder or just the same? >From your explanation it seems the optimization time is not an issue, especially considering you could turn it off after a while because the CLA would probably have already learned the correct control patterns. It could be turned on only when needed to improve the control and perhaps be done offline. If I recall correctly, Jeff wrote on one email that multiple step prediction is actually made by an external classifier, so it is not actually inherent to the CLA. Can someone clarify this point? Multiple step prediction is essential to MPC so I'd like to understand it better. Anyways, I've been pondering about my MPC idea and more and more I tend to believe that it is just too convoluted to work - I always favor simple solutions over complex ones. If we had motor control CLA I think this could be a great target for application, but it seems this is nowhere near our present. Perhaps training NuPIC on data from a classical controller such as PID or even manual control and then using a simple reinforcement learning procedure to train NuPIC's predictions in order to improve the control scheme (squared error and smoothness as you put it) would be a better solution, but I'm not clear on how this could be done. []'s Pedro. On Mon, Sep 2, 2013 at 3:32 PM, Matthew Taylor <[email protected]> wrote: > On Sep 2, 2013, at 11:05 AM, Ian Danforth <[email protected]> > wrote: > > > I'm going to be stupid in public... > > If only everyone were so fearless. :) > > Matt > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > -- Pedro Tabacof, Unicamp - Eng. de Computação 08.
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