| CPC G06N 3/082 (2013.01) [G06F 17/11 (2013.01); G06F 18/214 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01)] | 17 Claims |
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1. A system for self-constructing a neural network, comprising:
a processor;
a communication interface; and
a memory having executable code stored thereon, wherein the executable code, when executed by the processor, causes the processor to:
access a building block library, wherein the building block library is a hybrid hierarchical library comprising one or more library blocks having adaptability characteristics embedded, the one or more library blocks comprising flexible parent structures with configurable characteristics;
select, from the one or more library blocks, a first set of library blocks;
automatically generate a neural network using a self-constructing neural network architecture, wherein the neural network comprises the first set of library blocks;
provide a first set of input data to the neural network;
receive a first set of output data from the neural network;
detect that the first set of output data does not meet a termination condition; and
randomly modify, using a genetic algorithm, a structure within the neural network, wherein randomly modifying the structure within the neural network comprises an addition, removal, or substitution of at least one library block within the neural network;
provide a second set of input data to the neural network;
receive a second set of output data from the neural network;
detect that the second set of output data meets the termination condition;
continuously monitor the neural network for real-time changes in adversarial interaction patterns, the real-time changes comprising a rate of successful detection of unauthorized users, a rate of false positives, and decision-making speed; and
based on continuously monitoring the neural network for the real-time changes in adversarial interaction patterns, perform real-time optimization of the neural network, wherein the real-time optimization comprises fine-tuning performance of the neural network across plurality of performance parameters, the plurality of performance parameters comprising energy efficiency, heat generation, processing speed, and decisioning accuracy, wherein fine-tuning performance of the neural network comprises modifying an edge weight of at least one second block within the neural network;
monitor an effect of modifying the edge weight of the at least one second block on the plurality of performance parameters;
based on monitoring the effect of modifying the edge weight of the at least one second block on the plurality of performance parameters, detect a change in the one or more performance parameters; and
increase or decrease the edge weight of the at least one second block based on detecting the change in the plurality of performance parameters.
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