Abstract
The classification of helical structures is of fundamental importance in several domains including natural sciences, engineering and medicine. Helixes are complicated structures to separate due to their non-linear temporal nature. Several studies have examined the famous two-spiral problem using several types of neural architectures. It has been observed that neural networks can be reliably tested on such benchmarks to get estimates of their true ability for application for the real world problems. Past experiences on the use of neural networks has shown that the raw helix data needs significant transformation before good results are possible using a neural network. In this paper, we consider the classification of two spirals in three dimension using standard neural networks. The input features are extracted from the helix data on its direction and distance between successive points. The experiments test the neural network solution on a range of helixes that differ from the helix learnt to various extents.