NN with neurons that are designed as a Sum of Weighted Inputs is no good at solving problems where the Output Targets are, in part, a Product or a Function of multiple inputs. Is there a NN architecture that can solve problems like Target = Input1 * Input 2 + Input3? What do these architectures look like? Do these Input Products of multiple Inputs need to be represented as additional Inputs like Input6 = Input1 * Input2? Or should permutations of Input Products be treated as additional Hidden Layers as a Sum of Weighted Products of Inputs? Then what about Target = Input1 * Input2 * Input3 ? What is your advice?