Artificial Neural Networks: Artificial neural networks are represented by a set of nodes, often arranged in layers, and a set of weighted directed links connecting them. The nodes are equivalent to neurons, while the links denote synapses. The nodes are the information processing units and the links acts as communicating media.
There are a wide variety of networks depending on the nature of information processing carried out at individual nodes, the topology of the links, and the algorithm for adaptation of link weights. Some of the popular among them include:
Perceptron: This consists of a single neuron with multiple inputs and a single output. It has restricted information processing capability. The information processing is done through a transfer function which is either linear or non-linear.
Multi-layered Perceptron (MLP): It has a layered architecture consisting of input, hidden and output layers. Each layer consists of a number of perceptrons. The output of each layer is transmitted to the input of nodes in other layers through weighted links. Usually, this transmission is done only to nodes of the next layer, leading to what are known as feed forward networks. MLPs were proposed to extend the limited information processing capabilities of simple percptrons, and are highly versatile in terms of their approximation ability. Training or weight adaptation is done in MLPs using supervised backpropagation learning.
Recurrent Neural Networks: RNN topology involves backward links from output to the input and hidden layers. The notion of time is encoded in the RNN information processing scheme. They are thus used in applications like speech processing where inputs are time sequences data.
Self-Organizing Maps: SOMs or Kohonen networks have a grid topology, wit unequal grid weights. The topology of the grid provides a low dimensional visualization of the data distribution. These are thus used in applications which typically involve organization and human browsing of a large volume of data. Learning is performed using a winner take all strategy in a unsupervised mode.
In this module we will discuss perceptrons and multi layered perceptrons.
