Method and system for improving performance of an artificial neural network

A system for improving performance of an artificial neural network (ANN), the system comprising:

• an ANN improvement device comprising at least one processor and a computer-readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
• receiving a weight matrix comprising an original weight of each neural node in each layer of the ANN;
• for each unique combination of at least two neural nodes in each layer,
• determining a relative advantage value for one of the at least two neural nodes in a given layer with respect to remaining of the at least two neural nodes in the given layer based on actual inputs and standard inputs to the at least two neural nodes;
• and
• determining a modified weight of each of the at least two neural nodes based on the relative advantage value, wherein determining the modified weight of each of the at least two neural nodes comprises:
• increasing an original or a previously modified weight of the one of the at least two neural nodes by a first value proportional to the relative advantage value;
• and
• decreasing an original or a previously modified of each of the remaining of the at least two neural nodes by a second value proportional to the relative advantage value;
• executing an elimination decision for each neural node in each layer based on a corresponding final modified weight, wherein the final modified weight of a given neural node in a given layer is the modified weight of the given neural node upon exhausting the each unique combination of at least two neural nodes in the given layer;
• updating the weight matrix based on the final modified weight of each remaining neural node in each layer;
• and
• applying the ANN to an image recognition.

A non-transitory computer-readable medium storing computer-executable instructions for improving performance of an artificial neural network (ANN), the computer-executable instructions configured for:

• receiving a weight matrix comprising an original weight of each neural node in each layer of the ANN;
• for each unique combination of at least two neural nodes in each layer,
• determining a relative advantage value for one of the at least two neural nodes in a given layer with respect to remaining of the at least two neural nodes in the given layer based on actual inputs and standard inputs to the at least two neural nodes;
• and
• determining a modified weight of each of the at least two neural nodes based on the relative advantage value, wherein determining the modified weight of each of the at least two neural nodes comprises:
• increasing an original or a previously modified weight of the one of the at least two neural nodes by a first value proportional to the relative advantage value:
• and
• decreasing an original or a previously modified of each of the remaining of the at least two neural nodes by a second value proportional to the relative advantage value;
• executing an elimination decision for each neural node in each layer based on a corresponding final modified weight, wherein the final modified weight of a given neural node in a given layer is the modified weight of the given neural node upon exhausting each unique combination of the at least two neural nodes in the given layer;
• updating the weight matrix based on the final modified weight of each remaining neural node in each layer;
• and
• applying the ANN to an image recognition.