Designing Optimal Neuro-fuzzy Architectures for Intelligent Quality Control

The integration of the Artificial Neural Network (ANN) and Fuzzy Logic (FL) in one architecture in order, to overcome the individual limitations and to achieve synergetic effects through a combination of these techniques, has a in recent years contributed to a large number of Neuro-Fuzzy (NF) architectures. NF techniques override the classical control methods in many aspects, such as algorithm simplicity, system robustness and the ability to handle imprecision and uncertainties. In this paper are a presented some state-of-art NF models. A further attempt to assess the strengths and weakness of each NF architecture and selection criteria for IC applications is made. Finnalya choice of an optimal NF architecture is made and it’s future aplication in a quality control system is presented.