Artificial neural network

The term "Artificial Neural Network"(ANN) is derived from Biological neural networks. Similar to the human brain which has neurons interconnected to one another, artificial neural networks also have neurons that are interconnected to one another in various layers of the networks. These neurons are known as nodes.

Formally, ANN is a series of algorithms that mimics the human brain and find the relationship between a set of data. These are machine learning algorithms designed to acquire knowledge by extracting meaningful patterns or information from the dataset.

Artificial neuron- the building block of ANN

An artificial neuron is a mathematical function based on a model of biological neurons, where each neuron takes inputs, weighs them separately, sums them up and passes this sum through a nonlinear function to produce output.

Perceptron

The biological behavior of neurons can be captured by a simple model called perceptron.

This is an early supervised machine learning algorithm used for binary classifiers. This algorithm enables neurons to learn and processes elements in the training set one at a time.

Here x1, x2, x3 , …………………..xm are the inputs to the perceptron and w0, w1, w2, …………….wm are the weights attached to input links.

The perceptron model begins by multiplying all input values and their weights & adds these values to create the weighted sum. Further, this weighted sum is applied to the activation function to obtain the desired output.

Perceptron Learning Rule states that the algorithm would automatically learn the optimal weight coefficients. The Perceptron receives multiple input signals, and if the sum of the input signals exceeds a certain threshold (activation function), it either outputs a signal or does not return an output.

Architecture of ANN

Input Layer: It accepts inputs in several different formats provided by the programmer.

Hidden Layer: The hidden layer presents in-between input and output layers. It performs all the calculations to find hidden features and patterns.

Output Layer: The input goes through a series of transformations using the hidden layer, which finally produces output.

How ANN works-

Artificial Neural Network can be best represented as a weighted directed graph, where the artificial neurons forms the nodes. The association between the neurons outputs and neuron inputs can be viewed as the directed edges with weights. The Artificial Neural Network receives the input signal from the external source in the form of a pattern and image in the form of a vector. These inputs are then mathematically assigned by the notations x(n) for every n number of inputs. If the weighted sum is equal to zero, then bias is added to make the output non-zero.It is done to increase the system's performance. Bias has the same input, and weight equals to 1. Here the total of weighted inputs can be in the range of 0 to positive infinity.

Types of ANN

There are various types of Artificial Neural Networks (ANN) depending upon the human brain neuron and network functions, an artificial neural network similarly performs tasks.

Feedback ANN

In this type of ANN, the output returns to the network to accomplish the best-evolved results internally. The feedback networks feed information back into themselves and are well-suited to solve optimization issues. The Internal system error corrections utilize feedback ANNs.

Feed-Forward ANN

A feed-forward network is a basic neural network consisting of an input layer, an output layer, and at least one layer of a neuron i.e hidden layer. Through assessment of its output by reviewing its input, the intensity of the network can be noticed based on the group behavior of the associated neurons, and the output is decided.

Applications of ANN:

Following are some applications of artificial neural networks in real life-

• Speech Recognition

• Character Recognition

• Human Face Recognition

So today we learnt what is ANN, what is an artificial neuron , perceptron -learning rule, architecuture of ANN ,and types.

Thank you. See you in the next blog.Happy learning.