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Introduction to ANN | Set 4 (Network Architectures)

  • Last Updated : 09 Nov, 2021

Prerequisites: Introduction to ANN | Set-1, Set-2, Set-3 

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the brain. ANNs, like people, learn by examples. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning largely involves adjustments to the synaptic connections that exist between the neurons. 

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The model of Artificial neural network can be specified by three entities: 
 



Interconnections:

Interconnection can be defined as the way processing elements (Neuron) in ANN are connected to each other. Hence, the arrangements of these processing elements and geometry of interconnections are very essential in ANN. 
These arrangements always have two layers that are common to all network architectures, the Input layer and output layer where the input layer buffers the input signal, and the output layer generates the output of the network. The third layer is the Hidden layer, in which neurons are neither kept in the input layer nor in the output layer. These neurons are hidden from the people who are interfacing with the system and acts as a black box to them. On increasing the hidden layers with neurons, the system’s computational and processing power can be increased but the training phenomena of the system get more complex at the same time. 

There exist five basic types of neuron connection architecture : 

 

  1. Single-layer feed-forward network
  2. Multilayer feed-forward network
  3. Single node with its own feedback
  4. Single-layer recurrent network
  5. Multilayer recurrent network

1. Single-layer feed forward network 

 

In this type of network, we have only two layers input layer and output layer but the input layer does not count because no computation is performed in this layer. The output layer is formed when different weights are applied on input nodes and the cumulative effect per node is taken. After this, the neurons collectively give the output layer to compute the output signals.

2. Multilayer feed forward network 



 

This layer also has a hidden layer that is internal to the network and has no direct contact with the external layer. The existence of one or more hidden layers enables the network to be computationally stronger, feed-forward network because of information? ows through the input function, and the intermediate computations used to de?ne the output Z. There are no feedback connections in which outputs of the model are fed back into itself.

3. Single node with its own feedback 
 

Single Node with own Feedback
 

When outputs can be directed back as inputs to the same layer or preceding layer nodes, then it results in feedback networks. Recurrent networks are feedback networks with closed loops. The above figure shows a single recurrent network having a single neuron with feedback to itself.

4. Single-layer recurrent network 

 

The above network is a single-layer network with a feedback connection in which the processing element’s output can be directed back to itself or to another processing element or both. A recurrent neural network is a class of artificial neural networks where connections between nodes form a directed graph along a sequence. This allows it to exhibit dynamic temporal behavior for a time sequence. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs.

5. Multilayer recurrent network 
 

In this type of network, processing element output can be directed to the processing element in the same layer and in the preceding layer forming a multilayer recurrent network. They perform the same task for every element of a sequence, with the output being dependent on the previous computations. Inputs are not needed at each time step. The main feature of a Recurrent Neural Network is its hidden state, which captures some information about a sequence.

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