Continuous vs Discrete artificial neural networks

Question

I realize that this is probably a very niche question, but has anyone had experience with working with continuous neural networks? I\'m specifically interested in what a continu

Answer 1

In the past I've worked on a few research projects using continuous NN's. Activation was done using a bipolar hyperbolic tan, the network took several hundred floating point inputs and output around one hundred floating point values.

In this particular case the aim of the network was to learn the dynamic equations of a mineral train. The network was given the current state of the train and predicted speed, inter-wagon dynamics and other train behaviour 50 seconds into the future.

The rationale for this particular project was mainly about performance. This was being targeted for an embedded device and evaluating the NN was much more performance friendly then solving a traditional ODE (ordinary differential equation) system.

In general a continuous NN should be able to learn any kind of function. This is particularly useful when its impossible/extremely difficult to solve a system using deterministic methods. As opposed to binary networks which are often used for pattern recognition/classification purposes.

Given their non-deterministic nature NN's of any kind are touchy beasts, choosing the right kinds of inputs/network architecture can be somewhat a black art.