Information processing method, information processing system, and computer-readable non-transitory recording medium having information processing program recorded thereon

An information processing system, comprising:

• a first training part that trains a first neural network model so as to receive a first operation parameter for an operation of a first sensor and second sensing data obtained by an operation of a second sensor and output first sensing data obtained by the operation of the first sensor using the first operation parameter;
• a generation part that generates a third neural network model including the first neural network model and a second neural network model connected to each other in such a manner that the second neural network model receives the first sensing data output from the trained first neural network model and outputs an identification result of the first sensing data;
• a second training part that trains the second neural network model by backpropagation using an error difference between:
• the identification result which the third neural network model outputs after receiving the second sensing data and the first operation parameter;
• and correct identification information corresponding to the second sensing data;
• and
• an acquisition part that acquires a second operation parameter by updating the first operation parameter via the first neural network model by the backpropagation.

A non-transitory computer-readable storage medium that stores an information processing program for causing a computer to execute, by the information processing program, processing comprising:

• training a first neural network model so as to receive a first operation parameter for an operation of a first sensor and second sensing data obtained by an operation of a second sensor and output first sensing data obtained by the operation of the first sensor using the first operation parameter;
• generating a third neural network model including the first neural network model and a second neural network model connected to each other in such a manner that the second neural network model receives the first sensing data output from the trained first neural network model and outputs an identification result of the first sensing data;
• training the second neural network model by backpropagation using an error difference between:
• the identification result which the third neural network model outputs after receiving the second sensing data and the first operation parameter;
• and correct identification information corresponding to the second sensing data;
• and
• acquiring a second operation parameter by updating the first operation parameter via the first neural network model by the backpropagation.