Neuro-fuzzy control of complex manufacturing processes

A scheme for intelligent optimization and control of complex manufacturing processes is presented. The underlying nonlinear process is modelled by artificial neural networks and process control is performed by fuzzy logic. Fuzzy rules are automatically generated from the trained neural networks through a novel rule generation mechanism and fuzzy control is performed by Mamdani implication. Simulation results show that the proposed approach can provide a robust and accurate way of controlling complex processes without comprehensive models or knowledge about the process.