Neuro-Fuzzy Expert Systems: Overview with a Case Study

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.

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