Relationship discovery using Neural networks.

Question

Hi
Suppose I am sampling a number of signals at a fixed rate (say once per second) and extracting some metrics from the signals such as, ratio of one to the other, the rate of change, relative rate of change, etc.
I've heard that Neural Networks can be of use in discovering relationships. Is this true?
If so, what books/internet resources can I use to learn more about how do do this.
The processing is being done is Java, so a Java slant on all your answers would be most appreciated.
Thanks

Answer 1

That is true. You can discover relationships with NN. The problems is that it's very hard to interpret the weightings after you make the calibration.. so they are a bit of a black box (more so than other data mining algorithms).

I would actually recommend exploring the Neural Net algorithm that comes with MS Analysis Services. It's a good way to learn about NNets before you start programming anything (and since it's a server service you can call it from java).

Answer 2

Most likely you would need to determine some sort of a "window", like maybe the last 10 samples. You would normalize your signal into an array of 10 "doubles" normalized between -1 and 1. This would form the "input" into your neural network. So you would have 10 input neurons. Then you have to decide what you want the output to be. Maybe you have 100 different classifications that you may want to classify the signals into. If this is the case you would have 100 different output neurons that would each be trained to produce a higher output than the other output neurons when they recognize a specific signal.

Between the input and output layers neural networks usually have one or more hidden layers. These just provide additional capability to the neural network.

For Java neural network programming, you might try the Encog project. There is also a DotNet version of Encog as well.