xy z(0-15) 00 0101010101010101 01 0011001100110011 10 0000111100001111 11 0000000011111111
Here you have a simple neural network with 2-bit input and 16-bit output, it has 16 different logic output functions! that is, the greater the width at the entrance, the more options at the output. as a rule, we do not use everything, for example, only AND-NO, and this saves us.
Simplification is possible, in the end it’s just “input-output” correspondence, but all input options should be sorted out and for “big data” it is a task comparable to the training of the network.
although no, you already have a configured network. So, we need to start training a new network based on the findings of the configured network. and this NEW network can be optimized. and you understand the principles of construction and optimization.