A comparison of neural net and conventional techniques for lighting control
We compare two techniques for lighting control in an actual
room equipped with seven banks of lights and photoresistors to detect
the lighting level at four sensing points.
Each bank of lights can be independently set to one of sixteen intensity
levels. The task is to determine the device intensity levels that achieve
a particular configuration of sensor readings.
One technique we explored uses a neural network to approximate the mapping
between sensor readings and device intensity levels. The other
technique we examined uses a conventional feedback control loop.
The neural network approach appears superior both in that it does not require
experimentation on the fly (and hence fluctuating light intensity levels
during settling, and lengthy settling times) and in that it can
deal with complex interactions that conventional control techniques do not
handle well. This comparison was performed as part of the "Adaptive
House" project, which is described briefly.
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