# What is Artificial Neural Network – Structure, Working, Applications

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In this ANN Tutorial, we will learn Artificial Neural Network. Here, we will explore the working and structures of ANN. Moreover, we will discuss Artificial Neural Networks Applications & Types. Atlast, we will cover the Bayesian Network in AI.

So, let’s start the Artificial Neural Network Tutorial.

## What is Neural Network in Artificial Intelligence(ANN)?

ANN stands for Artificial Neural Networks. Basically, it’s a computational model. That is based on structures and functions of biological neural networks. Although, the structure of the ANN affected by a flow of information. Hence, neural network changes were based on input and output.

Basically, we can consider ANN as nonlinear statistical data. That means complex relationship defines between input and output. As a result, we found different patterns. Also, we call the ANN as a neural network.

## Structure of Artificial Neural Network

Generally, the working of a human brain by making the right connections is the idea behind ANNs. That was limited to use of silicon and wires as living neurons and dendrites.
Here, neurons, part of human brain. That was composed of 86 billion nerve cells. Also, connected to other thousands of cells by Axons. Although, there are various inputs from sensory organs. That was accepted by dendrites.
As a result, it creates electric impulses. That is used to travel through the Artificial neural network. Thus, to handle the different issues, neuron send a message to another neuron.

Basic Structure of Artificial Neural Network

As a result, we can say that ANNs are composed of multiple nodes. That imitate biological neurons of the human brain. Although, we connect these neurons by links. Also, they interact with each other.
Although, nodes are used to take input data. Further, perform simple operations on the data. As a result, these operations are passed to other neurons. Also, output at each node is called its activation or node value.
As each link is associated with weight. Also, they are capable of learning. That takes place by altering weight values. Hence, the following illustration shows a simple ANN −

Artificial Neural Network Structure

## Types of Artificial Neural Networks

Generally, there are two types of ANN. Such as FeedForward and Feedback.

### a. FeedForward ANN

In this network flow of information is unidirectional. A unit used to send information to another unit that does not receive any information. Also, no feedback loops are present in this. Although, used in recognition of a pattern. As they contain fixed inputs and outputs.

Types of Artificial Neural Networks – FeedForward ANN

### b. FeedBack ANN

In this particular Artificial Neural Network, it allows feedback loops. Also, used in content addressable memories.

Types of Artificial Neural Networks – FeedBack ANN

## How Does Artificial Neural Networks Works?

In this topology diagrams, you will learn everything in a detailed manner.
In this, each arrow represents a connection between two neurons. Also, they used to indicate the pathway for the flow of information. As it was noticed that each connection has a weight, an integer number. That used to controls the signal between the two neurons.
If the output is good that was generated by the network then we don’t require to adjust the weights. Although, if poor output generated. Then surely system will alter the weight to improve results.

## Machine Learning in ANNs

As there are too many Machine learning strategies are present, let’s see them one by one:

### a. Supervised Learning

Generally, in this learning a teacher is present to teach. That teacher must be aware of ANN.
For example:
The teacher feeds only example data. That teacher already knows the answers.

### b. Unsupervised Learning

If there is present no data set. Then we need this learning technique.

### c. Reinforcement Learning

As this Machine learning technique is based on the observation. Although, if it’s negative the networks need to adjust its weights. That is able to make a different required decision the next time.

## Back Propagation Algorithm

Generally, we use to call it as training and learning algorithm. As these networks are ideal for simple Pattern Recognition and Mapping Task.

## Bayesian Networks (BN)

Basically, we use to call it as graphical structures. Generally, we use this network to represent probabilistic representation. This represents among a set of random variables. Also, we used to call this network as Belief networks or Bayes Nets.
In these networks, each node represents a random variable with specific propositions.
In this only constraint arcs present in BN. Thus, doesn’t need to return node by following directed arcs.
Hence, we can say BNs are known as Directed Acyclic Graphs (DAGs). Hence, we use BNs to handle multivalued variables simultaneously.
Thus, BN variables composed of two dimensions −
• Range of prepositions
• Probability assigned to each of the prepositions.

## Artificial Neural Networks Applications

Artificial Neural Network used to perform a various task. Also, this task performs that are busy with humans but difficult for a machine.
a. Aerospace
Generally, we use ANN a for Autopilot aircrafts. They used for aircraft fault detection.
b. Military
In various ways, we use ANN an in the military. Such as Weapon orientation and steering, target tracking.
c. Electronics
Basically, we use an Artificial neural network in electronics in many ways. That are code sequence prediction, IC chip layout, and chip failure analysis.
d. Medical
As medical has too many machines. That use in various ways. Such as cancer cell analysis, EEG and ECG analysis.
e. Speech
We use ANN in speech recognition and speech classification.
f. Telecommunications
Generally, it has different applications. Thus, we use an Artificial neural network in many ways. Such as image and data compression, automated information services.
g. Transportation
Generally, we use an Artificial neural network in transportation in many ways. That are truck Brake system diagnosis and vehicle scheduling, routing systems.
h. Software
It also uses an ANN in pattern Recognition. Such as in facial recognition, optical character recognition, etc.
i. Time Series Prediction
We use an Artificial neural network to predict time. Also, we use ANNs to make predictions on stocks and natural calamities.
So, this was all about Artificial Neural Network (ANN) Tutorial. Hope you like our explanation

## Conclusion

As a result, we have studied Neural Network in Artificial Intelligence. Also, learned ANN structure, types and working. As this will help you to understand the Artificial Neural Networks concept deeply. Furthermore, if you feel any query, feel free to ask in the comment section.

### 13 Responses

1. Shruti Arora says:

hello,
I like your blog. you explain the application of artificial intelligence neural network really well. after reading, I am thinking to start learning neural network. I kindly request to tell me the application and limitation of artificial intelligence neural network in your upcoming blog.
thank you

• DataFlair Team says:

Hi Shruti,
Thanks for the wonderful feedback and suggestions for this AI tutorial. The applications of Artificial Neural Network are given in the article and for limitations, we will update this ANN tutorial soon.
Regards,
DataFlair

2. dinith says:

Hi, it is interesting and you have given a well structured description of artificial neural networks. I have learnt ANN upto some level and it is possible you to explain main differences between ANN neural networks and Deep learning networks in the sense of structure and leaning procedure.
Thank you

• DataFlair Team says:

Hi Dinith,
Artificial Neural Networks are computing systems inspired by biological neural networks. Like the human brain, they learn by examples, supervised or unsupervised. Deep Neural Networks are ANNs with a larger number of layers. Usually, we can call a network deep if it has at least 2 hidden layers. In some cases, this threshold can go up to 10 layers. Some deep neural networks may have thousands of layers. Each layer transforms the input into more abstract representations.
What makes this different from ANNs is the ability to learn complex features. This further justifies why DNNs are expensive and train on massive datasets.

3. DHIVYA says:

Artificial neural network is an algorithm or not?Its comes under Deep Learning or Machine Learning?Is there any coding associated with this for image processing?

• DataFlair Team says:

Hello Dhivya,
Artificial Neural Networks are a concept/ algorithm for Machine Learning. Deep Learning is a step ahead; Deep Neural Networks are similar to ANNs, but are made of a larger number of layers. However, we can safely say that usually, a deep neural network is one with at least 2 hidden layers. If you’d like to process images using neural networks and CNNs (Convolutional Neural Networks), OpenCV would be a good choice. You can import cv2 with Python for this.

4. Ihsan Khan says:

HI shruti…
you are explained two types of ANN but in some books ANN has five types …whose names is stochastic ANN,Recurrent ANN, FeedForword ANN,Module ANN and Dynamic Neural Network….
Regards:Ihsan khan

5. Ihsan Khan says:

But the rest off very best…Thank you

6. anusha says:

hello sir,

can you pl tell me that does the artificial neural network include programing also.If yes then which one.Will it be complex programming or simple.Regards

• DataFlair Team says:

Hi Anusha,
In projects involving ANNs, you will need to write code to train a model. You can do this in Python, making use of various classes and methods from different packages like scikit-learn and xgboost. This won’t be too difficult once you start to get the hang of it.
Hope, it helps!

7. Akshai says:

Sir,
can you please answer which all are the fields in neuroscience that uses machine learning

8. Alivi Milova says:

Hi Sir,
can you help me to provide information related to the ANN model of Adaptive Smoothing Neural Network? is there a link or journal that I can learn about?
Thankyou

9. seyed saneii says:

Hi
may I have the pdf file of the above document