Eric shared this with me on Gitter, thought I would post it: http://s1218.photobucket.com/user/222464/media/plot-1.png.html
Eric, feel free to explain if you like. Pretty good prediction of a sine wave. --------- Matt Taylor OS Community Flag-Bearer Numenta On Sun, Oct 26, 2014 at 6:29 PM, Eric Laukien <[email protected]> wrote: > Hello! > > In my quest to make a HTM based reinforcement learner, I need a value > function approximator. > I could just use a multilayer perceptron (MLP), but there is a problem with > this: MLPs forget old information readily in order to assimilate new > information (this is called "catastrophic forgetting"). A way around this is > by storing input/output pairs in a "experience" buffer, and then doing > stochastic sampling on that. But, this approach is inelegant, slow, and > requires a lot of memory. > > So, I have devised a new algorithm based on HTM's spatial pooler. I call it > SDRRBFNetwork (sparse distributed representation radial basis function > network). > > Essentially, it performs unsupervised learning using the continuous spatial > pooling algorithm I developed, and then uses a standard linear combination > of the SDR to get output. > > The main advantage of this is that there is almost no catastrophic > forgetting. Since only few cells receive attention at a time, most weights > are barely touched, keeping old information intact. > > I compared it to a standard MLP on a sine curve learning task. It needs to > produce the output of the sine curve for every input, but the inputs it is > given to train on are in order (high temporal coherence). SDRRBFNetwork got > 270 times less error than the MLP, in less training time. > > If that doesn't make a case for SDRs, I don't know what can!
