{"id":6045,"date":"2018-01-18T09:37:48","date_gmt":"2018-01-18T09:37:48","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=6045"},"modified":"2025-07-25T16:03:43","modified_gmt":"2025-07-25T10:33:43","slug":"artificial-neural-network","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/artificial-neural-network\/","title":{"rendered":"What is Artificial Neural Network – Structure, Working, Applications"},"content":{"rendered":"
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In this ANN Tutorial, we will learn Artificial\u00a0Neural Network. Here, we will explore the working and structures of ANN. Moreover, we will discuss\u00a0Artificial\u00a0Neural Networks Applications &\u00a0Types. Atlast, we will cover the Bayesian Network in AI.<\/p>\n

So, let’s start\u00a0the Artificial Neural Network Tutorial.<\/p>\n<\/div>\n

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What is Neural Network in Artificial Intelligence(ANN)?<\/h3>\n

ANN stands for Artificial Neural Networks<\/em>. Basically, it’s a computational model. That is based<\/span> 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<\/span> on input and output.<\/p>\n<\/div>\n

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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.<\/div>\n<\/div>\n
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Structure of Artificial Neural Network<\/h3>\n<\/div>\n
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Generally, the working of a human brain by making the right connections is the idea behind ANNs. That was limited<\/span> to use of silicon and wires as living neurons and dendrites.<\/div>\n
<\/div>\n<\/div>\n
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Here, neurons, part of human brain. That was composed<\/span> 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<\/span> dendrites.<\/div>\n
<\/div>\n
As a result, it creates electric impulses. That is used<\/span> to travel through the Artificial neural network. Thus, to handle the different issues, neuron send a message to another neuron.<\/div>\n<\/div>\n<\/div>\n
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\"Artificial<\/a>

Basic Structure of Artificial Neural Network<\/p><\/div>\n<\/div>\n

<\/div>\n
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As a result, we can say that ANNs are composed<\/span> of multiple<\/span> nodes. That imitate biological neurons of the human brain. Although, we connect these neurons by links. Also, they interact with each other.<\/div>\n
<\/div>\n
Although, nodes are used<\/span> to take input data. Further, perform simple operations on the data. As a result, these operations are passed<\/span> to other neurons. Also, output at each node is called<\/span> its activation or node value.<\/div>\n
<\/div>\n<\/div>\n
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As each link is associated<\/span> with weight. Also, they are capable of learning. That takes place by altering weight values. Hence, the following illustration shows a simple ANN \u2212<\/div>\n<\/div>\n<\/div>\n
<\/div>\n
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\"Basic<\/a>

Artificial Neural Network Structure<\/p><\/div>\n<\/div>\n

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Types of Artificial Neural Networks<\/h3>\n<\/div>\n
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Generally, there are two types of ANN. Such as FeedForward and Feedback.<\/div>\n<\/div>\n
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a. FeedForward ANN<\/h4>\n<\/div>\n
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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.<\/div>\n<\/div>\n<\/div>\n
<\/div>\n
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\"Types<\/a>

Types of Artificial Neural Networks –\u00a0FeedForward ANN<\/p><\/div>\n<\/div>\n

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b. FeedBack ANN<\/h4>\n<\/div>\n
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In this particular Artificial Neural Network, it allows feedback loops. Also, used in content addressable memories.<\/div>\n<\/div>\n<\/div>\n
<\/div>\n
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\"Types<\/a>

Types of Artificial Neural Networks – FeedBack ANN<\/p><\/div>\n<\/div>\n

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How Does Artificial Neural Networks Works?<\/h3>\n<\/div>\n
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In this topology diagrams, you will learn everything in a detailed manner.<\/div>\n<\/div>\n
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<\/div>\n
In this, each arrow represents a connection between two neurons. Also, they used to indicate<\/span> the pathway for the flow of information. As it was noticed<\/span> that each connection has a weight, an integer number. That used to controls the signal between the two neurons.<\/div>\n<\/div>\n
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<\/div>\n
If the output is good that was generated by<\/span> the network then we don\u2019t require<\/span> to adjust the weights. Although, if poor output generated. Then surely<\/span> system will alter the weight to improve results.<\/div>\n<\/div>\n
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Machine Learning in ANNs<\/h3>\n<\/div>\n
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As there are too many Machine learning strategies are present, let’s see them one by one:<\/div>\n<\/div>\n
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a. Supervised Learning<\/h4>\n
Generally, in this learning a teacher is present to teach. That teacher must be aware of ANN.<\/div>\n
<\/div>\n<\/div>\n
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For example:<\/strong><\/div>\n<\/div>\n
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The teacher feeds only example data. That teacher already knows the answers.<\/div>\n<\/div>\n
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b. Unsupervised Learning<\/h4>\n
If there is present no data set. Then we need this learning technique<\/span>.<\/div>\n<\/div>\n
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c. Reinforcement Learning<\/h4>\n
As this Machine learning technique is based<\/span> on the observation. Although, if it\u2019s negative the networks need to adjust its weights. That is able to make a different required decision the next time.<\/div>\n<\/div>\n
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Back Propagation Algorithm<\/h3>\n

Generally, we use to call it as training and learning algorithm. As these networks are ideal for simple Pattern Recognition and Mapping Task.<\/p>\n<\/div>\n

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Bayesian Networks (BN)<\/h3>\n<\/div>\n
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Basically<\/span>, 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.<\/div>\n<\/div>\n
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<\/div>\n
In these networks, each node represents a random variable with specific propositions.<\/div>\n<\/div>\n
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In this only constraint arcs present in BN. Thus, doesn\u2019t need to return node by following directed arcs.<\/div>\n
<\/div>\n
Hence, we can say BNs are known<\/span> as Directed Acyclic Graphs (DAGs). Hence, we use BNs\u00a0to handle multivalued variables simultaneously<\/span>.<\/div>\n<\/div>\n
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<\/div>\n
Thus, BN variables composed of two dimensions \u2212<\/strong><\/div>\n<\/div>\n
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    \n
  • Range of prepositions<\/li>\n<\/ul>\n<\/div>\n
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      \n
    • Probability assigned to each of the prepositions.<\/li>\n<\/ul>\n<\/div>\n
      \n

      Artificial Neural Networks Applications<\/h3>\n<\/div>\n
      \n
      Artificial Neural Network used to perform a various task. Also, this task performs that are busy with humans but difficult for a machine.<\/div>\n
      <\/div>\n<\/div>\n
      \n
      a. Aerospace<\/strong><\/div>\n
      <\/div>\n
      Generally, we use ANN\u00a0a for<\/span> Autopilot aircrafts. They used for aircraft<\/span> fault detection.<\/div>\n
      <\/div>\n<\/div>\n
      \n
      b. Military <\/strong><\/div>\n
      <\/div>\n
      In various ways, we use ANN an in the military. Such as Weapon orientation and steering, target tracking.<\/div>\n<\/div>\n
      \n
      <\/div>\n
      c. Electronics<\/strong><\/div>\n
      <\/div>\n
      Basically<\/span>, we use an Artificial neural network in electronics in many ways. That are code sequence prediction, IC chip layout, and chip failure analysis.<\/div>\n
      <\/div>\n<\/div>\n
      \n
      d. Medical <\/strong><\/div>\n
      <\/div>\n
      As medical has too many machines. That use<\/span> in various ways. Such as cancer cell analysis, EEG and ECG analysis.<\/div>\n
      <\/div>\n<\/div>\n
      \n
      e. Speech<\/strong><\/div>\n
      <\/div>\n
      We use ANN in speech recognition and speech classification.<\/div>\n
      <\/div>\n<\/div>\n
      \n
      f. Telecommunications<\/strong><\/div>\n
      <\/div>\n
      Generally, it has different applications. Thus, we use an Artificial neural network in many ways. Such as image and data compression, automated information services.<\/div>\n<\/div>\n
      \n
      <\/div>\n
      g. Transportation<\/strong><\/div>\n
      <\/div>\n
      Generally, we use an Artificial neural network in transportation in many ways. That are truck Brake system diagnosis and vehicle scheduling, routing systems.<\/div>\n<\/div>\n
      \n
      <\/div>\n
      h. Software<\/strong><\/div>\n
      <\/div>\n
      It also uses an ANN in pattern Recognition. Such as in facial recognition, optical character recognition, etc.<\/div>\n<\/div>\n
      \n
      <\/div>\n
      i. Time Series Prediction<\/strong><\/div>\n
      <\/div>\n
      We use an Artificial neural network to predict time. Also, we use ANNs to make predictions on stocks and natural calamities.<\/div>\n<\/div>\n<\/div>\n
      <\/div>\n
      So, this was all about Artificial Neural Network (ANN) Tutorial. Hope you like our explanation<\/div>\n
      \n
      \n

      Conclusion<\/h3>\n

      Artificial Neural Networks (ANNs) are models in AI that copy how our brain works. Just like our brain has neurons, ANN has tiny nodes that process information. These nodes are linked together in layers \u2014 input, hidden, and output layers. They help machines learn patterns and make smart decisions.<\/p>\n

      ANNs are used in many places like face recognition, handwriting reading, and voice assistants. For example, when you speak to Google Assistant, an ANN helps understand your words. It checks data from past examples and predicts what action to take. The more it practices, the better it gets.<\/p>\n

      Training a neural network means feeding it lots of data. It learns step by step and improves each time. But building a good neural network needs the right number of layers and correct settings. If done well, ANN can give powerful results, just like a trained brain working on a task.<\/p>\n

      Furthermore, if you feel any query, feel free to ask in the comment section.<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

      In this ANN Tutorial, we will learn Artificial\u00a0Neural Network. Here, we will explore the working and structures of ANN. Moreover, we will discuss\u00a0Artificial\u00a0Neural Networks Applications &\u00a0Types. Atlast, we will cover the Bayesian Network in...<\/p>\n","protected":false},"author":7,"featured_media":34939,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[706,1146,1166,4468,9054,9056],"class_list":["post-6045","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","tag-ann","tag-artificial-intelligence-neural-networks","tag-artificila-intelligence","tag-explained-neural-networks","tag-neural-network-in-artificial-intelligence","tag-neural-networks-in-a-i"],"yoast_head":"\nWhat is Artificial Neural Network - Structure, Working, Applications - DataFlair<\/title>\n<meta name=\"description\" content=\"Neural Network in Artificial Intelligence - What is Neural network and Artificial neural network, types of ANN, applications of neural networks,\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/artificial-neural-network\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What is Artificial Neural Network - 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