Process drift control in lithographic printing: issues and a connectionist expert system approach

Abstract Offset lithographic printing is the most widely used commercial printing process. The goal of the process is to attain the desired level of print quality and to maintain this level consistently for all subsequent prints. The role of the press operator is crucial in maintaining the print consistency, as this requires an understanding of the effects of the on-line adjustments made to the process on the outcoming prints. This research suggests the use of connectionist networks to dynamically capture the relationships between the process variables, and therefore, provide the knowledge base of an expert system with updated knowledge that is difficult to articulate and may vary throughout the lifetime of the machine. The properties of fault tolerance and generalization from noisy and/or incomplete data inherent to connectionist networks makes them practical for direct learning from actual process data.

[1]  Gary Josin,et al.  Integrating neural networks with robots , 1988 .

[2]  Gerald Tesauro,et al.  Connectionist Learning of Expert Preferences by Comparison Training , 1988, NIPS.

[3]  B. Bavarian,et al.  Introduction to neural networks for intelligent control , 1988, IEEE Control Systems Magazine.

[4]  Karl-Erik Årzén,et al.  Expert control , 1986, at - Automatisierungstechnik.

[5]  Abrahim Lavi,et al.  An Evolutionary Approach to Process Control with Application to Lithography , 1974, IEEE Trans. Syst. Man Cybern..

[6]  Neil A. Duffie,et al.  Computer Control of Machines and Processes , 1988 .

[7]  Soundar Kumara,et al.  Expert systems in industrial engineering , 1986 .

[8]  Marco Saerens,et al.  A neural controller , 1989 .

[9]  A. Guez,et al.  A neuromorphic controller with a human teacher , 1988, IEEE 1988 International Conference on Neural Networks.

[10]  Jirí Benes,et al.  On neural networks , 1990, Kybernetika.

[11]  Peter G. Raeth.  Expert systems in process observation and control , 1991 .

[12]  James H. Taylor,et al.  Rule-based real-time implementation of nonlinear self-synthesizing control systems , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.

[13]  R. Nakano,et al.  Medical diagnostic expert system based on PDP model , 1988, IEEE 1988 International Conference on Neural Networks.

[14]  Stephen I. Gallant,et al.  Connectionist expert systems , 1988, CACM.

[15]  T. Samad.  Towards connectionist rule-based systems , 1988, IEEE 1988 International Conference on Neural Networks.

[16]  Padhraic Smyth,et al.  An Information Theoretic Approach to Rule-Based Connectionist Expert Systems , 1988, NIPS.

[17]  Josiah C. Hoskins,et al.  Artificial neural network models for knowledge representation in chemical engineering , 1990 .

[18]  D. J. Shakley,et al.  Real-time expert systems for control using parallel processors , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.