They can perform tasks that are easy for a human but difficult for a machine −
● Aerospace − Autopilot aircrafts, aircraft fault detection.
● Automotive − Automobile guidance systems.
● Military − Weapon orientation and steering, target tracking, object discrimination, facial recognition, signal/image identification.
● Electronics − Code sequence prediction, IC chip layout, chip failure analysis, machine vision, voice synthesis.
● Financial − Real estate appraisal, loan advisor, mortgage screening, corporate bond rating, portfolio trading program, corporate financial analysis, currency value prediction, document readers, credit application evaluators.
● Industrial − Manufacturing process control, product design and analysis, quality inspection systems, welding quality analysis, paper quality prediction, chemical product design analysis, dynamic modeling of chemical process systems, machine maintenance analysis, project bidding, planning, and management.
● Medical − Cancer cell analysis, EEG and ECG analysis, prosthetic design, transplant time optimizer.
● Speech − Speech recognition, speech classification, text to speech conversion.
● Telecommunications − Image and data compression, automated information services, real-time spoken language translation.
● Transportation − Truck Brake system diagnosis, vehicle scheduling, routing systems.
● Software − Pattern Recognition in facial recognition, optical character recognition, etc.
● Time Series Prediction − ANNs are used to make predictions on stocks and natural calamities.
● Signal Processing − Neural networks can be trained to process an audio signal and filter it appropriately in the hearing aids.
● Control − ANNs are often used to make steering decisions of physical vehicles.
● Anomaly Detection − As ANNs are expert at recognizing patterns, they can also be trained to generate an output when something unusual occurs that misfits the pattern.