NET 19 is a neural network developed to make future predictions from a designated dataset. The neural network makes use of adaptive intelligence, also known as machine learning, to improve the accuracy of the model in real time.
The weightings the network operates on are designated into 2 sections. The network uses "Categories" for predictions. Each category has a designated weighting value. Within each category there are predefined "solutions". Each solution also has a designated weighting. The weighting of a category should be a decimal value < 1. The sum of all category weightings is == 1. The value for each category weighting is defined by the adaptive intelligence from the solutions within. Likewise the weighting of a category solution should be a decimal value < 1. The sum of all solutions weightings within a category == 1. The value of any category solution weighting is defined by adaptive intelligence.
When calculating the percentage chance, the magnitude of the category weighting is determined by the selected solution's weighting. E.g, a solution weighting of 0.3 means that the category weighting used in final calculation is only 30% of the total value.
