| CPC G06N 3/0442 (2023.01) [G06N 3/048 (2023.01); G06N 3/063 (2013.01); G06N 3/082 (2013.01)] | 30 Claims |
|
25. A method comprising:
configuring an Artificial Neural Network (ANN) section as a plurality of nodes respectively arranged into an input layer, an output layer, and at least one hidden layer interconnected between the respective input and output layers, each node in the ANN section having multiple inputs, a single output, and a non-differentiable activation function configured to emulate one or more Boolean logic functions responsive to a magnitude of the multiple inputs and a set of parametric values; and
training the ANN section using a chain isolation optimization process comprising identifying a selected node, detecting changes in an output error at an output node coupled to the selected node responsive to each of a different combination of the parametric values applied to the selected node, and calculating updated output values for each of a sequence of downstream nodes coupled along a chain path from the selected node to the output node using the output from the selected node and previously stored outputs from other nodes in the ANN section;
wherein each selected node combines input values from the multiple inputs using weight values for each input value, a bias value and a global precision value selected in relation to a desired precision between a minimum value and a maximum value to generate a weighted sum (WS), and wherein the non-differentiable activation function operates upon the WS to generate an associated output by the selected node; and
wherein the global precision value is characterized as a positive integer P, each of the input values ranges in magnitude from 0 to P, each of the weight values range in magnitude from −2P to +2P, the bias value ranges in magnitude from −1P to +3P, and the output of the selected node ranges in magnitude from 0 to P.
|