Neuro-fuzzy Hybrid

Neural Networks and Fuzzy logic represents two distinct methodologies to deal with uncertainty. Each of these has its own merits and demerits.

Neural Networks:

− Merits: Neural Networks can model complex nonlinear relationships and are appropriately suited for classification phenomenon into predetermined classes.

− Demerits: Neural Network's output, precision is often limited to least squares errors; the training time required is quite large; the training data has to be chosen over entire range where the variables are expected to change.

Fuzzy logic:

− Merits: Fuzzy logic system, addresses the imprecision of inputs and outputs defined by fuzzy sets and allow greater flexibility in formulating detail system description.

Integration of NN and FL, called Neuro-Fuzzy systems, have the potential to extend the capabilities of the systems beyond either of these two technologies applied individually. The integrated systems have turned out to be useful in :

− accomplishing mathematical relationships among many variables in a complex dynamic process,

− performing mapping with some degree of imprecision, and

− controlling nonlinear systems to an extent not possible with conventional linear control systems.

There are two ways to do hybridization:

− One is to provide NNs with fuzzy capabilities, thereby increasing the network's expressiveness and flexibility to adapt to uncertain environments.

− Second, is to apply neuronal learning capabilities to fuzzy systems so that the fuzzy systems become more adaptive to changing environments. This method is called NN driven fuzzy reasoning.