Neural simulation of a prototype keyboard assembly cell with adaptive control
Abstract
This paper describes the integration of a feedforward neural network with a real-time adaptive control technique to model an assembly cell for computer keyboards. The simulation model is able to undertake real-time forecasting of the expected behaviour of the cell, and also able to display in real time the current status of the assembly cell. The potential of integrating artificial neural networks into simulation together with an on-line real-time interface between the simulation model and the real-world system is investigated in this study. A simulation model was built in SIMAN with a neural network written in NeuralWork Professional II Plus embedded in the model to allow training and testing of the network. The control of the cell was achieved through a third-party program written in C that interfaces directly to the cell.