A shell environment for developing connectionist decision support systems

: This paper presents the architecture of a neural network expert system shell. The system captures every rule as a rudimentary neural network, which is called a network element (netel). The aim is to preserve the semantic structure of the expert system rules, while incorporating the learning capability of neural networks into the inferencing mechanism. These netel rules are dynamically linked up to form the rule-tree during the inferencing process, just as a conventional expert system does. The system is also able to adjust its inference strategy according to different users and situations. A rule editor is provided to enable easy maintenance of the netel rules. These components are housed under a user-friendly interface. An application

[1]  Hans W. Gottinger,et al.  Intelligent decision support systems , 1992, Decis. Support Syst..

[2]  Marco Guida,et al.  Knowledge-based decision support systems for manufacturing decision-making. The Pirelli experience , 1992 .

[3]  Robert W. Blanning,et al.  Decision support and knowledge-based systems , 1989 .

[4]  Efraim Turban,et al.  The Impact of Parallel and Neural Computing on Managerial Decision Making , 1989, J. Manag. Inf. Syst..

[5]  D. Schutzer.  Business expert systems: the competitive edge , 1990 .

[6]  Kathleen Foley Curley,et al.  The strategic use of expert systems for risk management in the insurance industry , 1992 .

[7]  Andrea Bonarini,et al.  Integrating expert systems and decision-support systems: principles and practice , 1991, Knowl. Based Syst..

[8]  Anca L. Ralescu,et al.  A connectionist approach for rule-based inference using an improved relaxation method , 1992, IEEE Trans. Neural Networks.

[9]  Izak Benbasat,et al.  Empirical Research in Managerial Support Systems: A Review and Assessment , 1993 .

[10]  Paul N. Finlay,et al.  IT for competitive advantage: the place of expert systems , 1992, J. Strateg. Inf. Syst..

[11]  Björn Möller,et al.  Knowledge systems and management decision support , 1991 .

[12]  D. King.  Intelligent decision support: strategies for integrating decision support, database mangement and expert system , 1990 .

[13]  Xingren Ying,et al.  Bond rating formulas derived through simplifying a trained neural network , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.