A lot of my time this week was spent struggling with implementing my neural network code in Simulink in order to experiment with using neural network control systems, as Rana suggested. Perhaps I'm inept at S-Functions, but I decided try to use the Model Reference Controller block in Simulink instead of my own code, and experiment with using that to control a driven, damped harmonic oscillator with noise. The block consists of two neural networks, one which is trained on a model of the plant to simulate the plant, and one that it trained on a reference model (which, in my case, is just input -> gain 0 -> output since my desired signal for now is 0. So far, I have managed to adjust the parameters enough to stop the neural network controller from outputting too much force (this causing the amplitude of the oscillator to increase with each iteration), but outputting enough to keep the plant oscillating with a maximal displacement of 2 m/s (with 30 neurons, 100 reference time delays, and 100 plant output time delays). I will continue to work on this, especially with added noise sources, and see how feasible it is to train such a controller to actually perform well.
As far as classification, I took up that project again, and realized that I have been approaching it the wrong way the whole time - instead of using a neural network to classify an entire time series, I could just classify it online using a recurrent neural network. This, however, meant that my data (which used to be in packets of 900 seconds) had to be parsed through to generate time-dependent desired output vectors. I did this last night, and have been trying various combinations of neuron numbers, time delays, and learning parameters this morning. Below is my current best result for mean square error with time, obtained with 1 neuron in the hidden layer, 50 time delays (so that there are actually 51 neurons feeding into the hidden layer, and a subnetwork of 50 neurons connected to 50 neurons, and learning parameter 0.7). The peak is due to the fact that a large amount of sharp earthquakes occur around that time, essentially giving the neural network a surprise, and causing it to rapidly learn. However, I suspect this sharp rise would decrease if I were to stop decimating my data by a factor of 256, and using all of the inputs as they come in (this, however would be drastically slower). Currently, I have a massive loop running which tries different combinations of neurons and time delays.
In terms of other lab stuff, Jenne and I ordered parts to make a cable for Gurlap-1, and I updated the pin map for Gurlap-1. Also, I wrote my progress report.