Neural network techniques for modeling sensor data

Some possible approaches to the use of neural networks for interpreting sensor, and particular spectral type, data are sketched. It is demonstrated how the structure of the neural network can be chosen to provide the capacity to fall back to the linear case when appropriate. An approach to the problem of underdetermined systems, based on adding random Gaussian noise to prevent the neural network from being locked into a local minimum, is presented