Types of Neural Network

Types of Neural Networks : 

There are two different types of neural network based on the learning mechanism -

Supervised and Unsupervised Neural Networks where in case of the former a desired dataset is provided("teacher") with which the model predicted data is compared to find level of accuracy of the model.The Unsupervised neural network will try to identify the inherent properties(data mining) of the input dataset with the help of different unsupervised learning methods.

Based on the signal transmission through the neural networks,neural network models can be divided into three distinct classes :

Feed forward - where the signal is transmitted only in the forward direction(Input-Hidden-Output);

Feedback - a feedback or error correction signal is transmitted back to the source so that deviation from the desired data is actuated and

Recurrent - signal is transmitted both ways.Recurrent Neural Networks can use their internal memory to process arbitrary sequences of inputs. The connections between units form a directed circle.This makes them applicable to tasks like unsegmented connected handwriting recognition .

Besides this classification there are some more special types of neural networks like : Radial Basis Functions,Hopfield Neural Network,Time delayed Neural Network etc.

Based on number of layers there can be two types of neural networks - Single and Multi-Layered where former has only one layer of input,hidden and outpit and the latter has more than one layers of hidden neurons. 

Summary : Types of Neural Network based on learning rule(Supervised and Unsupervised),signal processing process(feedforward,feedback & recurrent) and no. of layers(single & muliti) was discussed.