The XOR limit of the original perceptron

Once the feedforward network for solving the XOR problem is built, it will be applied to a material optimization business case. The material-optimizing solution will choose the best combinations of dimensions among billions to minimize the use of a material with the generalization of the XOR function. First, a solution to the XOR limitation of a perceptron must be fully clarified.

XOR and linearly separable models

In the academic world, like the private world, competition exists. Such a situation took place in 1969. Minsky and Papert published Perceptrons. They proved mathematically that a perceptron could not solve an XOR function. Fortunately, today the perceptron and its neocognitron version form the core model for neural networking.  

One might be tempted to think, So what? However, the entire field of neural networks relies on solving problems such as this to classify patterns. Without pattern classification, images, sounds, and words mean nothing...