Introduction:-The neural network models presented so far use variants of McCulloch and Pitt's neuron to build anetwork. New types of neurons have been introduced which use fuzzy membership functions asactivation functions or as functions attached to their connections. One of them is the so-called fuzzyneuron.
A fuzzy neuron has the following features, which distinguish it from the ordinary types of neurons:-
Excitatory connections are represented by MIN operation and inhibitory connections by fuzzy logic complements followed by MIN operation.
· A threshold level is not assigned. In the fuzzy neuron there is no learning. The membership functions attached to the synaptic connections do not change. The fuzzy neuron has been successfully used for handwritten character recognition.
The Neo-fuzzy neuron is a further development of the fuzzy neuron.
The features of the neo-fuzzy neuron are:
There are some training algorithms applicable to the neo-fuzzy neuron. One of them is called incremental updating (stepwise training).
Fuzzy neurons have been applied to prediction and classification problems.