The artificial neural network is yet another area of research related to the field of AI. With artificial intelligence emerging as the basis of every single task we do, it’s quite crucial to apprehend the meaning of this new terminology. The concept of neural network is stimulated from the natural neural network related to the nervous system of human. First we must understand the meaning of ANN.

Meaning of Artificial Neural Network

Artificial neural networks can be defined as the biologically stirred computer programs outlined to impel the way in which the human brain processes information. The artificial neural networks are compiled together to form the power of neural computations.

Artificial neural networks are considered to be one of the important tools exercised in the machine learning. As the “neural” segment of their name conveys, they are the systems that studies about brain-inspired systems. They tend to replicate the way how humans learn. Neural networks dwells on the layers of input and output or most of the time a hidden layer comprising of units that alters the input into a useful element that can be used by the output layer. These tools can be extremely helpful in hunting patterns that are too difficult for a human programmer to find or extract.

Types of Artificial Neural Networks

There are different types of Artificial Neural Networks (ANN) depending upon the human brain neuron and network functions:

Types Of Artificial Neural Network

  • Feedback ANN – This type of artificial neuron network involves the going back of output into the network to extract the best possible results within. The feedback network bolts the information tail to itself. It helps in solving the problems related to optimization.
  • Feed Forward ANN – This type of neural network is considered to be the simplest of all. This ANN is consists of an input, output and external layer of neurons. The power of Ann can be detected by the behavior of linked neurons. The benefit of this particular network is that it evaluates and recognizes the patterns of input.
  • Recurrent Neural Network – This neural network permits the bi-directional flow of data which is not possible in case of feed forward neural network. This type of network allows the compelling temporal behavior to be displayed.

Applications of Ann

The techniques of artificial neural network cater an alternative idea to get done with complexities among signal processing technologies. The list of applications of ANN is mentioned below:

  • The applications based on artificial neural networking are used in the sector of modeling and designing the solar steam developing plant.
  • These applications are proved to be useful in the system modeling like as in executing the difficult mapping and system identification.
  • Artificial neural networks are used for the reckoning of heating-loads of buildings etc.
  • There are diverse use of ANN techniques in pattern apprehension, recognition, robotics, power systems, manufacturing, medicine and many other segments.
  • The techniques of ANN can also be exercised to recognize the natural air flow and energy consumption of solar buildings.

Benefits of Artificial Neural Network

There are several advantages related to ANN such as:

  • The artificial neural networks are quite flexible and robust.
  • Artificial neural networks are exercised in array and pattern detection systems, data handling, robotics and modeling etc.
  • ANN acquires knowledge from their surroundings by adapting to internal and external parameters and they solve complex problems which are difficult to manage.
  • It helps in generalizing the knowledge to produce required responses to unexplained situations.

Conclusion

An artificial neural network is competent of greater defect tolerance as compared to a traditional network. The network can generate and recognize any defect or fault in any components without losing the stored data. Artificial neural networks have been outlined as the second best manner to scheme and design the interconnected neural. These artificial neural networks can be used to model the brains and complete certain computational tasks. The capacity of having character recognition can be achieved through a successful application of ANN.

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