A neural network classifier for recycling process

A neural-network-based material classification and sorting process is presented. The network is designed, built, and trained to classify four different recycling materials, i.e., plastic, aluminum, glass, and others into four classes. The network was tested on a set of measurements, and the network performance is graphically illustrated by plotting the actual measurements and the identified class for each measurement (neural net output). Detailed explanations including detection techniques, training rules, and procedures used to accomplish the task are presented