| CPC G06N 20/00 (2019.01) | 14 Claims |
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1. A method for identifying model optimization, the method comprising:
measuring, by a computing device, a plurality of subsystems, wherein the plurality of subsystems includes at least one remote device, and wherein measuring the plurality of subsystems produces a plurality of measurements;
comparing, by the computing device, each measurement of the plurality of measurements to a predetermined continuum range, wherein the predetermined continuum range comprises a lower threshold and an upper threshold;
identifying, by the computing device, an optimal measurement of the plurality of measurements and a suboptimal measurement of the plurality of measurements as a function of each of the comparisons;
generating, by the computing device, a positive feedback function of the optimal measurement, wherein generating the positive feedback function further comprises identifying, using an optimal machine-learning model, a first set of parameter changes to a subsystem corresponding to the optimal measurement, wherein the optimal machine-learning model is generated by updating a previous optimal machine learning model, wherein updating the previous optimal machine learning model comprises:
receiving optimal training data, wherein the optimal training data comprises a plurality of data entries containing a plurality of optimal measurements as inputs correlated to a plurality of positive feedbacks as outputs;
training the previous machine-learning model using the optimal training data;
sanitizing the optimal training data using a dedicated hardware unit comprising circuitry configured to perform signal processing operations, wherein sanitizing the optimal training data comprises:
determining by the dedicated hardware unit that a training data entry has a signal to noise ratio below a threshold value; and
removing the training data entry from the optimal training data;
retraining the previous machine-learning model using the sanitized training data; and
generating the positive feedback function as a function of the optimal measurement using the retrained optimal machine-learning model;
generating, by the computing device, a negative feedback function of the suboptimal measurement, wherein generating the negative feedback function comprises identifying, using a suboptimal machine-learning model, a second set of parameter changes to a subsystem corresponding to the suboptimal measurement; and
configuring the at least one remote device using the positive feedback function and the negative feedback function.
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