© Igor Livshin 2019
Igor LivshinArtificial Neural Networks with Javahttps://doi.org/10.1007/978-1-4842-4421-0_8

8. Approximating Noncontinuous Functions

Igor Livshin1 
(1)
Chicago, IL, USA
 

This chapter will discuss the neural network approximation of noncontinuous functions. Currently, this is a problematic area for neural networks because network processing is based on calculating partial function derivatives (using the gradient descent algorithm), and calculating them for noncontinuous functions at the points where the function value suddenly jump or drop leads to questionable results. We will dig deeper into this issue in this chapter. The chapter also includes a method I developed that solves this issue.

Example 5: Approximating Noncontinuous Functions ...

Get Artificial Neural Networks with Java: Tools for Building Neural Network Applications now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.