Differential Learning Expert System in Data Management

Differential Learning Expert System in Data Management

R. Manjunath
Copyright: © 2012 |Pages: 8
DOI: 10.4018/978-1-60960-818-7.ch306
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Abstract

Expert systems have been applied to many areas of research to handle problems effectively. Designing and implementing an expert system is a difficult job, and it usually takes experimentation and experience to achieve high performance. The important feature of an expert system is that it should be easy to modify. They evolve gradually. This evolutionary or incremental development technique has to be noticed as the dominant methodology in the expert-system area. The simple evolutionary model of an expert system is provided in B. Tomic, J. Jovanovic, & V. Devedzic, 2006. Knowledge acquisition for expert systems poses many problems. Expert systems depend on a human expert to formulate knowledge in symbolic rules. The user can handle the expert systems by updating the rules through user interfaces (J. Jovanovic, D. Gasevic, V. Devedzic, 2004). However, it is almost impossible for an expert to describe knowledge entirely in the form of rules. An expert system may therefore not be able to diagnose a case that the expert is able to. The question is how to extract experience from a set of examples for the use of expert systems.
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Background

Maintenance of databases in medium-size and large size organizations is quite involved in terms of dynamic reconfiguration, security, and the changing demands of its applications. Here, compact architecture making use of expert systems is explored to crisply update the database. An architecture with a unique combination of digital signal processing/information theory and database technology is tried. Neuro-fuzzy systems are introduced to learn “if-then-else” rules of expert systems.

Kuo, Wu, and Wang (2000) developed a fuzzy neural network with linguistic teaching signals. The novel feature of the expert system is that it makes use of a large number of previous outputs to generate the present output. Such a system is found to be adaptive and reconfigures fast. The expert system makes use of a learning algorithm based on differential feedback.

The differentially fed learning algorithm (Manjunath & Gurumurthy, 2002) is introduced for learning. The learning error is found to be minimal with differential feedback. Here, a portion of the output is fed back to the input to improve the performance. The differential feedback technique is tried at the system level, making the system behave with the same set of learning properties. Thus, control of an expert system controls the entire system

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