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Neuro-Fuzzy Expert Systems: Overview with a Case Study

  • Sushmita Mitra
  • Sankar K. Pal
Part of the International Series on Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 11)

Abstract

Artificial neural networks or connectionist models xc1,2,3 are massively parallel interconnections of simple neurons that function as a collective system. They are designed perhaps as an attempt to emulate human performance and function (itinter- ligently). An advantage of neural nets lies in their high computation rate provided by massive parallelism, so that real-time processing of huge data sets becomes feasible with proper hardware. Information is encoded among the various connec- tion weights in a distributed manner. The multilayer perceptron (MLP) xc2 is a feed-forward neural network model consisting of multiple payers of simple, sigmoid processing elements (nodes) or neurons. After a lowermost input layer there are usually any number of intermediate of hidden layers followed by an output later at the top. The learning procedure has to determine the internal parameters of the hidden units on its knowledge of the inputs and desired outputs.

An expert system xc4,5 is a computer program that functions in a narrow domain dealing with specialized knowledge generally possessed by human experts. Such programs are very useful due to the usual shortage of qualified human experts in real life. The primary characteristics of an experts systems are a knowledge base designed with the help of a human expert, a narrow problem domain, and a performance on par with a human expert. The knowledge base is a problem-specific module containing information that controls inferencing. Traditional rule-based expert systems encoded this information as If-Then rules while the connectionist expert system xc6 uses the set of connection weights of a trained neural net model for this purpose. The inference engine is problem independent while the user interface links the external environment to the system. Connectionist experts systems are usually suitable in data-rich environment. They help in minimizing human interaction and associated inherent bias during the phase of knowledge base formation (which is time-consuming in case of traditional models) and also reduce the possibility of generating contradictory rules. The rule generation phase of such connectionist models are usually completely automated. An expert system is expected to be able to draw conclusions without seeing all possible external information. It should be capable of directing the acquisition of new information in an efficient manner and also be able to justify a conclusion reached.

Keywords

Fuzzy Logic Expert System Connection Weight Certainty Factor Fuzzy Expert System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1994

Authors and Affiliations

  • Sushmita Mitra
    • 1
  • Sankar K. Pal
    • 1
  1. 1.Electronics and Communication Sciences Unit Indian Statistical InstituteElectronics and Communication Sciences Unit Indian Statistical InstituteCalcuttaIndia

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