Artificial Neural networks (ANN) or neural networks are computational algorithms.
It intended to simulate the behavior of biological systems composed of “neurons”. ANN are computational models inspired by an animal’s central nervous systems. It is capable of machine learning as well as pattern recognition. These presented as systems of interconnected “neurons” which can compute values from inputs.
A neural network is an oriented graph. It consists of nodes which in the biological analogy represent neurons, connected by arcs. It corresponds to dendrites and synapses. Each arc associated with a weight while at each node. Apply the values received as input by the node and define the activation function along the incoming arcs, adjusted by the weights of the arcs.
A neural network is a machine learning algorithm based on the model of a human neuron. The human brain consists of millions of neurons. It sends and process signals in the form of electrical and chemical signals. These neurons are connected with a special structure known as synapses. Synapses allow neurons to pass signals. From large numbers of simulated neurons, neural networks forms.
An Artificial Neural Network is an information processing technique. It works like the way the human brain processes information. ANN includes many, numerous of connected processing units that work together to process information. They also generate meaningful results from it. We can apply Neural network not only for classification. It can also apply for regression of continuous target attributes.
Neural networks find great application in data mining used in sectors. For example economics, forensics, etc. and for pattern recognition. It can also be used for data classification in a large amount of data after careful training.
A neural network may contain the following 3 layers:
- Input layer – The activity of the input units represents the raw information that can feed into the network.
- Hidden layer – To determine the activity of each hidden unit. The activities of the input units and the weights on the connections between the input and the hidden units. There may be one or more hidden layers.
- Output layer – The behavior of the output units depends on the activity of the hidden units and the weights between the hidden and output units.
Input Layer
The purpose of the input layer is to receive as input the values of the explanatory attributes for each observation. Usually, the number of input nodes in an input layer is equal to the number of explanatory variables. The input layer presents the patterns to the network, which communicates to one or more ‘hidden layers’.
The nodes of the input layer are passive, meaning they do not change the data. They receive a single value on their input and duplicate the value to their many outputs. From the input layer, it duplicates each value and sent to all the hidden nodes.
Hidden layer
The Hidden layers apply given transformations to the input values inside the network. In this, incoming arcs that go from other hidden nodes or from input nodes connected to each node. It connects with outgoing arcs to output nodes or to other hidden nodes. In hidden layer, the actual processing is done via a system of weighted ‘connections’. There may be one or more hidden layers. The values entering a hidden node multiplied by weights, a set of predetermined numbers stored in the program. The weighted inputs are then added to produce a single number.
Output layer
The hidden layers then link to an output layer. Output layer receives connections from hidden layers or from input layer. It returns an output value that corresponds to the prediction of the response variable. In classification problems, there is usually only one output node. The active nodes of the output layer combine and change the data to produce the output values.
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